Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Engineering (21775)
- Civil and Environmental Engineering (7878)
- Electrical and Computer Engineering (5103)
- Physical Sciences and Mathematics (3766)
- Geotechnical Engineering (3713)
-
- Chemical Engineering (2359)
- Materials Science and Engineering (2244)
- Mechanical Engineering (2231)
- Structural Engineering (2023)
- Computer Sciences (1475)
- Mining Engineering (1187)
- Aerospace Engineering (1155)
- Civil Engineering (1051)
- Operations Research, Systems Engineering and Industrial Engineering (846)
- Earth Sciences (767)
- Architecture (764)
- Metallurgy (748)
- Architectural Engineering (740)
- Chemistry (710)
- Biochemical and Biomolecular Engineering (703)
- Geology (668)
- Ceramic Materials (643)
- Petroleum Engineering (612)
- Physics (590)
- Social and Behavioral Sciences (554)
- Geological Engineering (369)
- Manufacturing (336)
- Nuclear Engineering (334)
- Public Affairs, Public Policy and Public Administration (325)
- Energy Policy (320)
- Keyword
-
- Electromagnetic Interference (101)
- Machine learning (97)
- Electromagnetic Compatibility (92)
- Additive manufacturing (90)
- Neurocontrollers (86)
-
- Optimal Control (83)
- Optimization (78)
- Rheology (74)
- Neural Nets (67)
- Neural Networks (63)
- Simulation (62)
- Deep learning (61)
- Additive Manufacturing (58)
- Computer Simulation (58)
- Machine Learning (58)
- Power System Control (58)
- Finite Difference Time-Domain Analysis (56)
- Stability (55)
- Mathematical Models (53)
- Printed Circuit Boards (53)
- Printed Circuits (53)
- Modeling (52)
- Microstructure (51)
- Adaptive Control (49)
- EMI (48)
- Nonlinear Control Systems (48)
- Concrete (46)
- Mechanical properties (46)
- Power Electronics (46)
- Voltage Control (46)
- Publication Year
- Publication
-
- Masters Theses (3811)
- Electrical and Computer Engineering Faculty Research & Creative Works (3465)
- The Missouri Miner Newspaper (3259)
- International Conference on Case Histories in Geotechnical Engineering (2056)
- Doctoral Dissertations (1777)
-
- International Conferences on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics (1566)
- Mechanical and Aerospace Engineering Faculty Research & Creative Works (1289)
- Civil, Architectural and Environmental Engineering Faculty Research & Creative Works (1277)
- CCFSS Proceedings of International Specialty Conference on Cold-Formed Steel Structures (1971 - 2018) (1036)
- Materials Science and Engineering Faculty Research & Creative Works (1028)
- Chemical and Biochemical Engineering Faculty Research & Creative Works (855)
- Computer Science Faculty Research & Creative Works (765)
- Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works (525)
- Engineering Management and Systems Engineering Faculty Research & Creative Works (492)
- UMR-MEC Conference on Energy / UMR-DNR Conference on Energy (417)
- Physics Faculty Research & Creative Works (414)
- Missouri S&T Magazine (404)
- Plant Identification in a Combined-Imbalanced Leaf Dataset -- Images (374)
- Professional Degree Theses (338)
- Chemistry Faculty Research & Creative Works (337)
- Bachelors Theses (326)
- Mining Engineering Faculty Research & Creative Works (316)
- American Iron and Steel Institute (AISI) Specifications, Standards, Manuals and Research Reports (1946 - present) (237)
- CCFSS Library (1939 - present) (234)
- Undergraduate Research Conference at Missouri S&T (188)
- Opportunities for Undergraduate Research Experience Program (OURE) (187)
- Mathematics and Statistics Faculty Research & Creative Works (164)
- Symposia on Turbulence in Liquids (126)
- International Symposia on Low Cost Housing Problems (119)
- Minutes & Agendas (119)
- Publication Type
- File Type
Articles 1711 - 1740 of 29096
Full-Text Articles in Entire DC Network
Graph Convolutional Neural Network Assisted Genetic Algorithm For Pdn Decap Optimization, Haran Manoharan, Jack Juang, Ling Zhang, Hanfeng Wang, Jingnan Pan, Kelvin Qiu, Xu Gao, Chulsoon Hwang
Graph Convolutional Neural Network Assisted Genetic Algorithm For Pdn Decap Optimization, Haran Manoharan, Jack Juang, Ling Zhang, Hanfeng Wang, Jingnan Pan, Kelvin Qiu, Xu Gao, Chulsoon Hwang
Electrical and Computer Engineering Faculty Research & Creative Works
This paper proposes a hybrid algorithm combining reinforcement learning (RL) and a genetic algorithm (GA) for PDN decap optimization. The trained RL agent uses a graph convolutional neural network as a policy network and predicts the decap solution for a given PDN impedance and target impedance, which is seeded as an initial population to the GA. The trained RL agent is scalable regarding the number of decap ports. The main goal is to save computation time and find the near global minimum or global minimum. Generalization of the algorithm to different decap libraries is achieved through transfer learning, eventually reducing …
Relative Altitude Estimation Of Infrared Thermal Uav Images Using Sift Features, Shirin Nasr Esfahani, Jagannathan Sarangapani
Relative Altitude Estimation Of Infrared Thermal Uav Images Using Sift Features, Shirin Nasr Esfahani, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Unmanned Aerial Vehicles (UAVs) have become indispensable in various applications, including surveillance, urban scene analysis, and agricultural monitoring. Accurate altitude estimation is critical for UAV operations, especially in environments where traditional sensors like GPS, pressure altimeters, and radar may fail. This paper explores the use of infrared and thermal imaging for relative altitude estimation of UAVs, highlighting their significant advantages over traditional RGB images. Infrared and thermal imaging offer superior performance in low-light and adverse weather conditions, providing clearer visibility and more reliable feature detection. By leveraging the Scale-Invariant Feature Transform (SIFT) features, this approach utilizes the inherent benefits of …
Lifelong Direct Error-Driven Learning For Uav Altitude Estimation In Different Weather Conditions, Shirin Nasr-Esfahani, Jagannathan Sarangapani
Lifelong Direct Error-Driven Learning For Uav Altitude Estimation In Different Weather Conditions, Shirin Nasr-Esfahani, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
While deep neural networks achieve remarkable visual perception capabilities for UAV position and orientation estimation, their resilience to different weather conditions still needs improvement. These models often suffer from catastrophic forgetting when adapted to new environments, losing previously acquired knowledge. Lifelong learning methods aim to balance learning flexibility and memory stability. In this paper, we present an image-based approach to estimate the relative altitude of a UAV using 2D images under varying weather conditions, including sunny, sunset, and foggy scenarios. Our experiments demonstrate significant performance degradation when the model is trained sequentially on different weather datasets, especially when new images …
Suppression Of Power Distribution Network Pcb-Package Resonance For Low Target Impedance, Francesco De Paulis, Faye Squires, Yifan Ding, Matteo Cocchini, Matthew Doyle, Samuel Connor, Albert E. Ruehli, Chulsoon Hwang, Lijun Jiang
Suppression Of Power Distribution Network Pcb-Package Resonance For Low Target Impedance, Francesco De Paulis, Faye Squires, Yifan Ding, Matteo Cocchini, Matthew Doyle, Samuel Connor, Albert E. Ruehli, Chulsoon Hwang, Lijun Jiang
Electrical and Computer Engineering Faculty Research & Creative Works
The suppression of the large resonance peak that may appear due to the equivalent parallel circuit between the package capacitance and PCB inductance is discussed. Such resonance may be amplified if the decoupling capacitors are not appropriately selected. The relevant parameters involved in the PDN design, and a feasible solution strategy are presented based on the identification of a simplified equivalent circuit that is able to replicate the resonant behavior. The optimization of the relevant parameters of such circuit are able to suggest the best strategy for identifying the decoupling capacitors with appropriate values of parasitic inductance and resistance.
Instantaneous Current And Average Power Flow Characterization Of A Dc-Dc-Dc Triple Active Bridge Converter, Jonathan Saelens, Lauryn Morris, Oroghene Oboreh-Snapps, Arnold Fernandes, Praneeth Uddarraju, Sophia A. Strathman, Jonathan W. Kimball
Instantaneous Current And Average Power Flow Characterization Of A Dc-Dc-Dc Triple Active Bridge Converter, Jonathan Saelens, Lauryn Morris, Oroghene Oboreh-Snapps, Arnold Fernandes, Praneeth Uddarraju, Sophia A. Strathman, Jonathan W. Kimball
Electrical and Computer Engineering Faculty Research & Creative Works
The Triple Active Bridge (TAB) is a Three-Port Power Converter that Facilitates Bi-Directional Power Flow and Provides Galvanic Isolation, Making It a Subject of Significant Research Attention. This is Attributed to its Diverse Applications in High-Frequency DC-DC Conversion, Electric Vehicles, Renewable Energy Integration, and Micro-Grids. Controlling the System at Run-Time Involves Modification of the Two Phase-Shift Parameters between Respective Bridges. by Analyzing the Fundamental Converter Operating Equations, Future Control Designers Can Use This Framework to Optimize Control Schemes to Mitigate the Under-Determined Nature of the TAB Converter. in This Paper, We Elucidate the Foundational Operational Principles of the TAB and …
Multilayer Ceramic Capacitor Vibration Source Model Library Development, Yifan Ding, Ming Feng Xue, Jianmin Zhang, Xin Hua, Benjamin Leung, Eric A. Macintosh, Chulsoon Hwang
Multilayer Ceramic Capacitor Vibration Source Model Library Development, Yifan Ding, Ming Feng Xue, Jianmin Zhang, Xin Hua, Benjamin Leung, Eric A. Macintosh, Chulsoon Hwang
Electrical and Computer Engineering Faculty Research & Creative Works
When a Multilayer Ceramic Capacitor (MLCC) is Soldered on a Printed Circuit Board (PCB), the Capacitor Deformation Generates a Force on the PCB, Which Serves as a Source to Excite PCB Vibration and Generate Unwanted Acoustic Noise. a MLCC Can Be Used under Any Loading Conditions as Long as the Power Supply Voltage Remains Below its Rated Voltage; Thus, the MLCC Source Mode Can Be Extremely Complicated Because of Different Combinations Including Direct Current Voltage Levels, Alternating Current Noise Amplitudes, and Related Frequencies. When the Power Rail is under Different Loading Conditions, the PCB Vibration Amplitude Can Differ; However, the …
Improved Peec Modeling Of Antennas Through Time-Dependent Partial Elements, Fabrizio Loreto, Giuseppe Pettanice, Martin Stumpf, Albert E. Ruehli, Jonas Ekman, Giulio Antonini
Improved Peec Modeling Of Antennas Through Time-Dependent Partial Elements, Fabrizio Loreto, Giuseppe Pettanice, Martin Stumpf, Albert E. Ruehli, Jonas Ekman, Giulio Antonini
Electrical and Computer Engineering Faculty Research & Creative Works
Over the Last Twenty Years, the Evolution of Communications Has Extended the Systems' Operating Frequency Range to the Tens of GHz. in This Framework, Time Domain Integral Equation-Based (TDIE) Methods for Antenna Modeling Have Gained Increasing Interest. among Them, the Partial Elements Equivalent Circuit (PEEC) Method Turns Out to Be Attractive for its Capability to Provide Compact Circuit Models. Similarly to Other Integral Equation-Based Methods, Like the Method of Moments (MoM) in the Time Domain (TD), the PEEC Method Can Suffer from Late-Time Instabilities. This Work Shows that a Rigorous Computation of the Time-Dependent Partial Elements Leads to an Improved …
Optimal Trajectory Tracking For Uncertain Linear Discrete-Time Systems Using Time-Varying Q-Learning, Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan
Optimal Trajectory Tracking For Uncertain Linear Discrete-Time Systems Using Time-Varying Q-Learning, Maxwell Geiger, Vignesh Narayanan, Sarangapani Jagannathan
Electrical and Computer Engineering Faculty Research & Creative Works
This Article Introduces a Novel Optimal Trajectory Tracking Control Scheme Designed for Uncertain Linear Discrete-Time (DT) Systems. in Contrast to Traditional Tracking Control Methods, Our Approach Removes the Requirement for the Reference Trajectory to Align with the Generator Dynamics of an Autonomous Dynamical System. Moreover, It Does Not Demand the Complete Desired Trajectory to Be Known in Advance, Whether through the Generator Model or Any Other Means. Instead, Our Approach Can Dynamically Incorporate Segments (Finite Horizons) of Reference Trajectories and Autonomously Learn an Optimal Control Policy to Track Them in Real Time. to Achieve This, We Address the Tracking Problem …
Oxygen Fugacity Of Global Ocean Island Basalts, Lori N. Willhite, Ricardo Arevalo, Philip Piccoli, John C. Lassiter, Devin Rand, Matthew G. Jackson, James M.D. Day, Robert W. Nicklas, Marek Locmelis, Thomas J. Ireland, Igor S. Puchtel
Oxygen Fugacity Of Global Ocean Island Basalts, Lori N. Willhite, Ricardo Arevalo, Philip Piccoli, John C. Lassiter, Devin Rand, Matthew G. Jackson, James M.D. Day, Robert W. Nicklas, Marek Locmelis, Thomas J. Ireland, Igor S. Puchtel
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Mantle plumes contain heterogenous chemical components and sample variable depths of the mantle, enabling glimpses into the compositional structure of Earth's interior. In this study, we evaluated ocean island basalts (OIB) from nine plume locations to provide a global and systematic assessment of the relationship between fO2 and He-Sr-Nd-Pb-W-Os isotopic compositions. Ocean island basalts from the Pacific (Austral Islands, Hawaii, Mangaia, Samoa, Pitcairn), Atlantic (Azores, Canary Islands, St. Helena), and Indian Oceans (La Réunion) reveal that fO2 in OIB is heterogeneous both within and among hotspots. Taken together with previous studies, global OIB have elevated and heterogenous fO …
Borate-Based Bioactive Glasses Properties: Clinical And Biomedical Applications, Sarah Fakher, David J. Westenberg
Borate-Based Bioactive Glasses Properties: Clinical And Biomedical Applications, Sarah Fakher, David J. Westenberg
Biological Sciences Faculty Research & Creative Works
Borate bioactive glasses (BBGs) constitute the recently produced bioactive glasses (BGs) in the domain of bioactive materials, offering enhanced functionalities over conventional silicate and phosphate BGs. This advancement is primarily characterized by BBGs' rapid yet controllable degradation rates, superior bioactivity, and the ability to swiftly form a hydroxyapatite layer (HAP) upon interaction with physiological fluids. A distinguishing feature of BBGs lies in their customizability; doping with therapeutic metal ions tailors their bioactivity towards specific physiological responses, including promoting angiogenesis, antibacterial activity, and osteogenesis. This versatility of BBGs' applications extends to bone regeneration, wound healing, drug delivery systems, and potentially nerve …
Spark Plasma Sintering Of Magnesium Titanate Ceramics, Suzana Filipović, Nina Obradović, William G. Fahrenholtz, Steven Smith, Miljana Mirković, Adriana Peleš Tadić, Jovana Petrović, Antonije Đorđević
Spark Plasma Sintering Of Magnesium Titanate Ceramics, Suzana Filipović, Nina Obradović, William G. Fahrenholtz, Steven Smith, Miljana Mirković, Adriana Peleš Tadić, Jovana Petrović, Antonije Đorđević
Materials Science and Engineering Faculty Research & Creative Works
Magnesium titanate ceramics were prepared by reactive spark plasma sintering (SPS) at 1200 °C for 5 min. Prior to sintering, MgO and TiO2 powders were mixed by high energy ball milling (HEBM) for 15, 30, or 60 min. The effect of milling time on phase composition was analyzed by X-ray diffraction (XRD) for milled powders and sintered specimens. The morphology of the sintered ceramics was investigated by scanning electron microscopy (SEM), while elemental distribution was determined by energy dispersive spectroscopy (EDS). The presence of the MgTi2O5 phase was detected in XRD and was confirmed by EDS analysis. Microcracking was …
Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi
Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi
Mathematics and Statistics Faculty Research & Creative Works
Cluster Analysis Has Been Applied To A Wide Range Of Problems As An Exploratory Tool To Enhance Knowledge Discovery. Clustering Aids Disease Subtyping, I.e. Identifying Homogeneous Patient Subgroups, In Medical Data. Missing Data Is A Common Problem In Medical Research And Could Bias Clustering Results If Not Properly Handled. Yet, Multiple Imputation Has Been Under-Utilized To Address Missingness, When Clustering Medical Data. Its Limited Integration In Clustering Of Medical Data, Despite The Known Advantages And Benefits Of Multiple Imputation, Could Be Attributed To Many Factors. This Includes Methodological Complexity, Difficulties In Pooling Results To Obtain A Consensus Clustering, Uncertainty Regarding …
An Ai-Based Conceptual Framework To Improve Program Management Of Complex Systems, Michael D. Parrish, Steven Corns
An Ai-Based Conceptual Framework To Improve Program Management Of Complex Systems, Michael D. Parrish, Steven Corns
Engineering Management and Systems Engineering Faculty Research & Creative Works
With evolving technologies, changing requirements, and limited budgets, governments and industries need to consider new methodologies to help streamline program lifecycle management, from cradle to grave, to ensure projects are delivered on time, on budget, and to the expected performance standards. Traditional approaches fail to adequately address the added complexities of System of Systems programs such as integration, interoperability, and variable lifecycle of subcomponents. The objective of this study is to assess and address the research question - can a new acquisition approach be designed to address and improve program lifecycle management of complex systems? A comparison study, using the …
Navigating Innovation Within A Contractor's Business Model: A System Dynamics Approach, Mariam Elazhary, Cihan Dagli, Islam H. El-Adaway
Navigating Innovation Within A Contractor's Business Model: A System Dynamics Approach, Mariam Elazhary, Cihan Dagli, Islam H. El-Adaway
Engineering Management and Systems Engineering Faculty Research & Creative Works
Innovation is essential in the construction industry, but current practices often focus narrowly on specific aspects rather than taking a comprehensive approach. To address this, systematic innovation through business model innovation (BMI) is proposed to enhance overall performance. Despite increasing interest in BMI research, it remains in its early stages, lacking quantitative analysis of its impact on business model components. This paper develops a framework using system dynamics to assess changes to a construction company's BM when faced with a variety of innovations across various projects. This shall be accomplished by (1) establishing factors and their relationships that define a …
A Systematic Review Of Phenotypic And Epigenetic Clocks Used For Aging And Mortality Quantification In Humans, Brandon Warner, Edward Ratner, Anirban Datta, Amaury Lendasse
A Systematic Review Of Phenotypic And Epigenetic Clocks Used For Aging And Mortality Quantification In Humans, Brandon Warner, Edward Ratner, Anirban Datta, Amaury Lendasse
Engineering Management and Systems Engineering Faculty Research & Creative Works
Aging is the leading driver of disease in humans and has profound impacts on mortality. Biological clocks are used to measure the aging process in the hopes of identifying possible interventions. Biological clocks may be categorized as phenotypic or epigenetic, where phenotypic clocks use easily measurable clinical biomarkers, and epigenetic clocks use cellular methylation data. In recent years, methylation clocks have attained phenomenal performance when predicting chronological age and have been linked to various age-related diseases. Additionally, phenotypic clocks have been proven to be able to predict mortality better than chronological age, providing intracellular insights into the aging process. This …
A Model-Adaptive Random Search Actor Critic: Convergence Analysis And Inventory-Control Case Studies, Yuehan Luo, Jiaqiao Hu, Abhijit Gosavi
A Model-Adaptive Random Search Actor Critic: Convergence Analysis And Inventory-Control Case Studies, Yuehan Luo, Jiaqiao Hu, Abhijit Gosavi
Engineering Management and Systems Engineering Faculty Research & Creative Works
Reinforcement learning (RL) is an exciting area within the domain of Markov Decision Processes (MDPs) in which the underlying optimization problem is solved either in a simulator of the real-world system or via direct interaction with the real-world system, when its underlying transition probabilities are difficult to estimate. The latter is commonly true of large-scale, real-world MDPs with complex underlying transition dynamics. RL is currently being widely researched in the world of medicine/neuroscience after some spectacular success stories demonstrating super-human behavior in computer games. In this paper, we propose a new actor-critic-based RL algorithm for approximately solving continuous state/action MDPs …
A Simulation-Based Digital Twin For Data-Driven Maintenance Scheduling Of Risk-Prone Production Lines Via Actor Critics, Abhijit Gosavi, Aparna Gosavi
A Simulation-Based Digital Twin For Data-Driven Maintenance Scheduling Of Risk-Prone Production Lines Via Actor Critics, Abhijit Gosavi, Aparna Gosavi
Engineering Management and Systems Engineering Faculty Research & Creative Works
Industry 4.0 mandates a shift from traditional total productive maintenance (TPM) methods, which rely on periodic data gathering and subsequent offline modeling for maintenance scheduling, towards more data-driven and online decision-making approaches. Further, Industry 4.0 emphasizes reducing intervention from high-skilled managers and leveraging real-time data for decision-making, which is characteristic of models rooted in either renewal-theoretic or Markov chains for traditional TPM methods. Digital Twins (DTs), which are virtual representations of physical systems, play a crucial role in this paradigm by enabling online decision-making of maintenance scheduling directly at the workstation level. In this paper, a simulation-based DT (S-DT) is …
The Impact Of Journalistic Cultures On Social Media Discourse: Us Primary Debates In Cross-Lingual Online Spaces, Lea Hellmueller, Lindita Camaj, Sebastián Vallejo Vera, Peggy Lindner
The Impact Of Journalistic Cultures On Social Media Discourse: Us Primary Debates In Cross-Lingual Online Spaces, Lea Hellmueller, Lindita Camaj, Sebastián Vallejo Vera, Peggy Lindner
Engineering Management and Systems Engineering Faculty Research & Creative Works
This cross-lingual project examines how social media posts of Spanish- and English-language media impact incivility in user comments during the 2020 primary political debates in the United States. We analyzed Facebook posts of news organizations that hosted the debates and used a state-of-the-art machine-learning model to analyze the corresponding comments. Our findings reveal distinct journalistic cultures on the post-level: English-language media are significantly more likely to use interpretation while Spanish-language media employ more audience-engagement and factual reporting strategies. We argue that in order to understand incivility in social media discourse during political debates, we need to consider journalistic cultures: While …
Inspire Newsletter Fall 2024, Missouri University Of Science And Technolgy Inspire - University Transportation Center
Inspire Newsletter Fall 2024, Missouri University Of Science And Technolgy Inspire - University Transportation Center
INSPIRE Newsletters
No abstract provided.
Development Of A Deep Neural Network And Empirical Model For Predicting Local Gas Holdup Profiles In Bubble Columns, Sebastián Uribe, Ahmed Alalou, Mario E. Cordero, Muthanna H. Al-Dahhan
Development Of A Deep Neural Network And Empirical Model For Predicting Local Gas Holdup Profiles In Bubble Columns, Sebastián Uribe, Ahmed Alalou, Mario E. Cordero, Muthanna H. Al-Dahhan
Chemical and Biochemical Engineering Faculty Research & Creative Works
Estimating local gas holdup profiles in bubble columns is key for their performance evaluation and optimization, as well as for design and scale-up tasks. Up to the current day, there are important limitations in the accuracy and range of applicability of the available models in literature. Two alternatives for the prediction of such local fields can be found in the application of empirical models and the development of deep neural networks (DNN). The main drawback preventing the application of these techniques in previous years was the availability of a large enough databank of local gas holdup experimental measurements. Advances over …
Pharmacology Of Boldine: Summary Of The Field And Update On Recent Advances, Juan C. Sáez, Justin C. Burrell, Catherine M. Cahill, D. Kacy Cullen, Lakshmi A. Devi, Ryan J. Gilbert, Zachary A. Graham, Vadim J. Gurvich, Leif A. Havton, Ravi Iyengar, Rajesh Khanna, Edmund F. Palermo, Mustafa Siddiq, Carlos A. Toro, Walter Vasquez, Wei Zhao, Christopher P. Cardozo
Pharmacology Of Boldine: Summary Of The Field And Update On Recent Advances, Juan C. Sáez, Justin C. Burrell, Catherine M. Cahill, D. Kacy Cullen, Lakshmi A. Devi, Ryan J. Gilbert, Zachary A. Graham, Vadim J. Gurvich, Leif A. Havton, Ravi Iyengar, Rajesh Khanna, Edmund F. Palermo, Mustafa Siddiq, Carlos A. Toro, Walter Vasquez, Wei Zhao, Christopher P. Cardozo
Chemical and Biochemical Engineering Faculty Research & Creative Works
Over the past decade, boldine, a naturally occurring alkaloid found in several plant species including the Chilean Boldo tree, has garnered attention for its efficacy in rodent models of human disease. Some of the properties that have been attributed to boldine include antioxidant activities, neuroprotective and analgesic actions, hepatoprotective effects, anti-inflammatory actions, cardioprotective effects and anticancer potential. Compelling data now indicates that boldine blocks connexin (Cx) hemichannels (HCs) and that many if not all of its effects in rodent models of injury and disease are due to CxHC blockade. Here we provide an overview of boldine's pharmacological properties, including its …
Advanced Detection Of Sars-Cov-2 And Omicron Variants Via Mxene-Graphene Hybrid Biosensors Utilizing Nucleic Acid Probes, Jiaoli Li, Yuwei Zhang, Congjie Wei, Yanxiao Li, Zhekun Peng, Hsin Yin Chuang, Logan Pearce, Adrianus Boon, Yue-Wern Huang, Dong Hyun Kim, Risheng Wang, Chenglin Wu
Advanced Detection Of Sars-Cov-2 And Omicron Variants Via Mxene-Graphene Hybrid Biosensors Utilizing Nucleic Acid Probes, Jiaoli Li, Yuwei Zhang, Congjie Wei, Yanxiao Li, Zhekun Peng, Hsin Yin Chuang, Logan Pearce, Adrianus Boon, Yue-Wern Huang, Dong Hyun Kim, Risheng Wang, Chenglin Wu
Biological Sciences Faculty Research & Creative Works
Low-cost biosensors that can rapidly and widely detect viruses are critical for faster diagnosis and treatment decision-making, especially for infections. The commonly used field-effect transistor is sensitive to the biomarker's detection but struggles with precise detection, particularly of nontargets such as ions and proteins. To overcome this limitation, we developed a field-effect transistor biosensor design based on MXene-graphene materials to increase the accuracy and sensitivity of virus detection. Based on the hybridization process between two complementary DNA strands, single-stranded nucleic acids were immobilized on the sensing surface via 3-aminopropyltriethoxysilane and glutaraldehyde to capture the nucleic acids of the target virus. …
Identifying The Job Characteristics Affecting Construction Firm Employee Turnover Intention, Seog Jae Choi
Identifying The Job Characteristics Affecting Construction Firm Employee Turnover Intention, Seog Jae Choi
Civil, Architectural and Environmental Engineering Faculty Research & Creative Works
Researchers have proved that employees who have the intention to quit the organization, called turnover intention, have decreased productivity and may not fulfill their duties properly. The turnover intention has a negative relationship with the quality of core job characteristics of the organization. As the construction industry has distinctive characteristics compared to other industries, the job characteristics that influence turnover intention need to be explored separately. This study identifies job characteristics of the construction industry that can affect turnover intention. Through the literature review, autonomy, task identity, required skill variety, task significance, justification in assigning work location, and job security …
Mobility Management In Tsch-Based Industrial Wireless Networks, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Mobility Management In Tsch-Based Industrial Wireless Networks, Marco Pettorali, Francesca Righetti, Carlo Vallati, Sajal K. Das, Giuseppe Anastasi
Computer Science Faculty Research & Creative Works
Wireless Sensor and Actuator Networks (WSANs) are an effective technology for improving the efficiency and productivity in many industrial domains and are also the building blocks for the Industrial Internet of Things (IIoT). To support this trend, the IEEE has defined the 802.5.4 Time-Slotted Channel Hopping (TSCH) protocol. Unfortunately, TSCH does not provide any mechanism to manage node mobility, while many current industrial applications involve Mobile Nodes (MNs), e.g., mobile robots or wearable devices carried by workers. In this article, we present a framework to efficiently manage mobility in TSCH networks, by proposing an enhanced version of the Synchronized Single-hop …
Energy Consumption Optimization Of Uav-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning, Xiangjie Kong, Chenhao Ni, Gaohui Duan, Guojiang Shen, Yao Yang, Sajal K. Das
Energy Consumption Optimization Of Uav-Assisted Traffic Monitoring Scheme With Tiny Reinforcement Learning, Xiangjie Kong, Chenhao Ni, Gaohui Duan, Guojiang Shen, Yao Yang, Sajal K. Das
Computer Science Faculty Research & Creative Works
Unmanned Aerial Vehicles (UAVs) can capture pictures of road conditions in all directions and from different angles by carrying high-definition cameras, which helps gather relevant road data more effectively. However, due to their limited energy capacity, drones face challenges in performing related tasks for an extended period. Therefore, a crucial concern is how to plan the path of UAVs and minimize energy consumption. To address this problem, we propose a multi-agent deep deterministic policy gradient based (MADDPG) algorithm for UAV path planning (MAUP). Considering the energy consumption and memory usage of MAUP, we have conducted optimizations to reduce consumption on …
Stitching Satellites To The Edge: Pervasive And Efficient Federated Leo Satellite Learning, Mohamed Elmahallawy, Tony Tie Luo
Stitching Satellites To The Edge: Pervasive And Efficient Federated Leo Satellite Learning, Mohamed Elmahallawy, Tony Tie Luo
Computer Science Faculty Research & Creative Works
In the Ambitious Realm of Space AI, the Integration of Federated Learning (FL) with Low Earth Orbit (LEO) Satellite Constellations Holds Immense Promise. However, Many Challenges Persist in Terms of Feasibility, Learning Efficiency, and Convergence. These Hurdles Stem from the Bottleneck in Communication, Characterized by Sporadic and Irregular Connectivity between LEO Satellites and Ground Stations, Coupled with the Limited Computation Capability of Satellite Edge Computing (SEC). This Paper Proposes a Novel FL-SEC Framework that Empowers LEO Satellites to Execute Large-Scale Machine Learning (ML) Tasks Onboard Efficiently. its Key Components Include I) Personalized Learning Via Divide-And-Conquer, Which Identifies and Eliminates Redundant …
Trusted Digital Twin Network For Intelligent Vehicles, Asad Malik, Ayan Roy, Sanjay Madria
Trusted Digital Twin Network For Intelligent Vehicles, Asad Malik, Ayan Roy, Sanjay Madria
Computer Science Faculty Research & Creative Works
Vehicle-to-vehicle (V2V) infrastructure facilitates wireless communication among vehicles within close proximity. This allows sharing of contextual information such as speed, location, direction, traffic, route closures, human behavior mental conditions to improve traffic flow, reduce collisions, and enhance safety on the road. However, the assumption of honest peers along with the over-reliability on the information shared in the network can pose a serious threat to human safety. A digital twin is a concept that enables a system to develop a virtual environment that mimics the real-life scenario for any situation. The availability of powerful computing equipment inside vehicles can be leveraged …
Mobilytics: Mobility Analytics Framework For Transferring Semantic Knowledge, Shreya Ghosh, Soumya K. Ghosh, Sajal K. Das, Prasenjit Mitra
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) …
Real-Time Analysis Of Encrypted Dns Traffic For Threat Detection, Marta Moure-Garrido, Sajal K. Das, Celeste Campo, Carlos Garcia-Rubio
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 …
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
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 …