Engineering Commons^™

Doing Less With More: A First-Principles Exploration Of The Suitability Of Agentic Computing Over Alternative Architectural Choices, Ritvik Garimella, Biplav Srivastava, Amit Sheth

Publications

There is growing interest in automating business activities with Agentic Artificial Intelligence (AI) due to latter's seeming ease of use. However, little is known on when they are suitable for a task over other alternatives developed over the years - local computation, REstful State Transfer (REST), and Simple Object Access Protocol (SOAP) - considering development speed, performance, and operational cost. We explore this with a small mathematical task evaluating five methods for automated mathematical expression evaluation across a benchmark of 1,000 equations where semantics of operator precedence has to be preserved. We ran this setup across a native Function Calling …

One Size Does Not Fit All: Revisitingworld Models And Neurosymbolic Ai, Amit P. Sheth, Madhur Thareja, Anushka Pawar, Niyati Rawal

Publications

World models are being built twice, from opposite ends, without a shared theory of how the two halves should meet. One lineage grounds the world model in perception: a self-supervised, latent-predictive encoder – exemplified by Joint Embedding Predictive Architectures (JEPA) – that learns the structure of sensory experi-ence. A second, older lineage grounds the world model in cognition: an explicit, inspectable structure of entities, rules, and constraints, ranging from knowledge graphs to formal logic to physical law. Neither lineage alone has produced a world model that is simultane-ously adaptive and auditable. We argue this is not solved by picking a …

Automated Guided Wave Characterization Of Marcelling: In-Plane Fiber Waviness In Thermoplastic Composites, Sourav Banerjee, Ana Tudor, Corey Leydig, Thomas Ferguson, Tally Bovender, Darun Barazanchy, Josh Widosky, Paul Ziehl

Faculty Publications

Thermoplastic composite (TPC) materials often develop in-plane fiber waviness during manufacturing, a defect known as marcelling. Marcelling negatively impacts the strength and overall performance of the composite and are hard to control. While it may appear as a surface imperfection, it can also extend partially through the thickness of the material. Currently, there is no established nondestructive evaluation (NDE) method to reliably detect and quantify marcelling in composites after manufacturing. As marcelling is not a local defect but spans across the surface in in-plane dimensions of the structure, traditional pulse-echo and phased-array ultrasonic NDE are ineffective. Automated multi-directional guided …

Understanding The Binding Of Nickel(Ii) Bromide To A Zirconium Metal-Organic Cage For Heterogeneous Catalysis, Luci Green

Caroliniana Undergraduate Research Journal

Heterogeneous catalysts offer inherent advantages in improving the sustainability of industrial chemical processes. Their high ease of separation and recyclability has the potential to reduce the cost, waste, and energy consumption of processes that currently rely on homogeneous catalysts. Metal–organic cages (MOCs) are an attractive material for heterogeneous catalysis due to their discrete and highly tunable structures, which can be functionalized by binding these complexes with catalytically active metals. The specific objective of this study is to provide preliminary insight into the binding of nickel(II) bromide (NiBr₂) to zirconium MOCs, which was done by studying the binding of …

Tunable Shock And Vibration Damping In Metal Laser Powder Bed Fusion Components Via Controlled Post-Print Compaction Of Particle Damper Cavities, Joud N. Stame, Yanzhou Fu, Samuel Roberts, Austin Downey, Tianyu Zhang, Lang Yuan, Daniel Kiracofe

Faculty Publications

Components manufactured by Laser Powder Bed Fusion (LPBF) can incorporate particle dampers (PDS) by retaining unfused powder within internal pockets, providing inherent vibration suppression without added mass or separate damping components. This study investigates the damping performance of such LPBF-integrated PDs through two complementary approaches: (1) quantifying the effect of post-printing volume compression on energy absorption by mechanically indenting the damper pocket, and (2) evaluating how variations in particle packing density influence the dynamic response under both sinusoidal and transient impulse excitation. Experimental results from shaker and shock testing demonstrate that increased packing density, whether by compression or tighter confinement, …

Roadmap On Artificial Intelligence-Augmented Additive Manufacturing, Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei-Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C.L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano-Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan

Faculty Publications

Artificial intelligence-augmented additive manufacturing (AI2AM) represents a transformative frontier in digital fabrication, where artificial intelligence (AI) is embedded not as a peripheral tool, but as a central framework driving intelligent, adaptive, and autonomous additive manufacturing (AM) systems. The objective of this Roadmap is to present a comprehensive vision of the state-of-the-art developments in AI2AM while charting the future trajectory of this rapidly emerging field. As AM applications continue to expand across diverse sectors, conventional design and control strategies face growing limitations in scalability, quality assurance, and material complexity. AI uses tools like computer vision, generative design, and large language models …

AiXGa1−XN (0.7 < X < 1) Schottky Diodes Using Distributed Polarization Doped Layer With A Current Density Of 14 Ka/Cm2, Tariq Jamil, Abdullah Al Mamun Mazumder, Mafruda Rahman, Muhammad Ali, Grigory Simin, M. Asif Khan

Faculty Publications

High Al-content Al_xGa_1-xN (0.7 < x < 1) quasi-vertical Schottky barrier diodes (SBDs) with distributed polarization doping were grown on the bulk AlN substrate. They exhibit excellent rectification behavior with a large forward current density (~14 kA/cm² ) and a high breakdown field of ~8.3 MV/cm. The SBDs also exhibited low ideality factors of (n~1.2) with a high Schottky barrier height (Φ _b~ 1.7 eV). Thus, this study demonstrates the feasibility of the distributed polarization doping approach for high current–high voltage devices.

Mechanical Performance Of Equilateral Triangular Lattices: The Role Of Nodal Fillets, Fakhreddin Emami, Andrew J. Gross

Faculty Publications

Triangular lattices are widely employed for their high strength to weight ratios, yet their mechanical performance is sensitive to geometric features, particularly the nodal geometry. This study investigates the influence of nodal geometry on the mechanical behavior of equilateral triangular lattices across a broad range of relative densities using high fidelity finite element simulations. We characterize the elastic properties, and strength limits as functions of fillet radius. Our results confirm the expected trend that, in stretching-dominated lattices with low relative density, the introduction of fillets reduces both stiffness and buckling resistance. In contrast, at higher relative densities, filleted nodes can …

Roadmap: Integrating Artificial Intelligence In Structural Health Monitoring Systems, Simon Laflamme, Erik Blasch, Flippo Ubertini, Zheng Liu, John Wertz, Christine Knott, Matthew Cherry, Eric Lindgren, Fu-Kuo Chang, Amrita Kumar, Jack Poole, Keith Worden, Austin Downey, Jie Wei, Patrick F. Musgrave, Adrian S. Wong, Guiseppe Quaranta, Marco Martino Rosso, Giuseppe Carlo Marano, Yu Chen, Et. Al.

Faculty Publications

Advances in computing and machine learning (ML) methods have led to a rapid rise in artificial intelligence (AI) research and applications in many fields. AI research benefitted from advances in computation hardware, collection and distribution of large data sets, and proliferation of software techniques. AI techniques include ML for provable results, deep learning for data exploration, reinforcement learning for control, and active learning for adaptive systems. Likewise, AI algorithms can handle large amounts of data, construct unknown representations, and provide a direct link between data and classification for decision making. These unmatched capabilities have been seen as a path to …

Towards Sustainable Energy Storage: Evaluating The Performance Of Three Polymer Electrolytes For Zinc-Ion Batteries, Roya Rajabi, Shichen Sun, Buke Sun, Jamil A. Khan, Kevin Huang

Faculty Publications

Polymer electrolytes have been explored as an alternative to conventional aqueous electrolytes in zinc-ion batteries, particularly for flexible and wearable applications. Despite the increasing interest in polymer electrolyte-based zinc-ion batteries (ZIBs), their development is still in its early stages due to various challenges. In this study, we investigated three promising polymer electrolytes: CSAM (carboxyl methyl chitosan with acrylamide monomer), PAM (polyacrylamide monomer hydrogel electrolyte), and p-PBI (phosphate-doped polybenzimidazole solid electrolyte) with Zn(ClO₄)₂ and Zn(OTf)₂, as electrolytes for zinc-ion batteries. The p-PBI solid electrolyte showed high mechanical stability and improved resistance to short-circuiting during cycling. The …

Towards Sustainable Energy Storage: Evaluating The Performance Of Three Polymer Electrolytes For Zinc-Ion Batteries, Roya Rajabi, Shichen Sun, Buke Wu, Jamil A. Khan, Kevin Huang

Faculty Publications

Polymer electrolytes have been explored as an alternative to conventional aqueous electrolytes in zinc-ion batteries, particularly for flexible and wearable applications. Despite the increasing interest in polymer electrolyte-based zinc-ion batteries (ZIBs), their development is still in its early stages due to various challenges. In this study, we investigated three promising polymer electrolytes: CSAM (carboxyl methyl chitosan with acrylamide monomer), PAM (polyacrylamide monomer hydrogel electrolyte), and p-PBI (phosphate-doped polybenzimidazole solid electrolyte) with Zn(ClO4)2 and Zn(OTf)2, as electrolytes for zinc-ion batteries. The p-PBI solid electrolyte showed high mechanical stability and improved resistance to short-circuiting during cycling. The presence of carboxyl groups in …

Beyond Catalysts And Membranes: Using Cell Assembly And Operating Strategies To Significantly Improve The Performance Of Aem Electrolyzers, Mahmoud Amirsalehi, Venkata Sai Sriram Mosali, Mohammed Al-Murisi, Shaghayegh Bashiri, Hari Gopi, Pongsarun Satjaritanun, Benjamin Britton, Karen Swider-Lyons, Mustain E. William

Faculty Publications

Green hydrogen, produced through water electrolysis, is a key enabler of a low-carbon energy future, and anion exchange membrane electrolyzers (AEMELs) have emerged as a promising technology due to their potential for ultra-low-cost operation. However, achieving low cell voltage at high current densities and maintaining long-term durability remain key AEMEL challenges. To address these issues, most research efforts to date have focused on developing advanced catalysts and membranes. In contrast, the influence of non-material factors, such as cell assembly parameters and operating conditions, remains underexplored, even though they can significantly impact performance. This study investigates how such variables affect AEMEL …

Impediments To Transforming The Healthcare Delivery System: Shifting The Paradigm From Provider Centric To Patient Centric, Elizabeth A. Regan, Manasa Devi Chinta

Faculty Publications

Introduction: 

Stated aims for digital healthcare transformation frequently cite goals for better coordinated patient-centric systems. However, despite advances in medical science, digital technologies, health policies, and billions of dollars invested over the past 25 years, most healthcare providers are far from fully realizing the demonstrated benefits of today's digital technologies for improving patient care. Sharing information across healthcare systems remains challenging. Problems with fragmentation, quality, inequities, and rising costs of care delivery persist. A recent study of 1,026 U.S. hospital systems found that only 15.8 percent achieved a digital maturity level needed to provide digitally enabled healthcare services to better …

Influence Of Pulse Width On Energy Deposition And Temperature In Nanosecond-Pulsed Discharges, Christopher B. Reuter, Joshua B. Sinrud, Tanvir I. Farouk, Nicholas S. Dewey, Dmitri Kaganovich

Faculty Publications

Nanosecond-pulsed discharges are a promising method to enhance combustion but can generate significant levels of electromagnetic interference (EMI). Modifying the discharge pulse width is an unexplored option to reduce EMI, but few studies have examined how changing the pulse width affects discharge parameters such as energy and temperature. This study addresses this issue by systematically investigating how the pulse width affects the energy per pulse, breakdown time, rotational temperature, and vibrational temperature in air across different frequencies, flow velocities, and gap distances in a plasma-assisted flow tube. It is observed that the pulse width has a substantial impact on the …

Hybrid Cnn–Gru-Based Demand–Supply Forecasting To Enhance Sustainability In Renewable-Integrated Smart Grids, Süleyman Emre Eyimaya, Necmi Altin

Faculty Publications

The rapid integration of renewable energy sources in smart grids has introduced significant uncertainty in both power generation and consumption patterns, posing challenges to environmental, economic, and operational sustainability. Accurate short-term forecasting of energy demand and supply is essential for achieving optimal scheduling, grid stability, and resilient operation in renewable-integrated power systems. This study proposes a hybrid deep learning framework combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) for intelligent joint demand–supply forecasting in smart grids. The model was developed and implemented in MATLAB using real-world datasets comprising electricity consumption, photovoltaic (PV) generation, temperature, and irradiance variables. Comparative …

Bridging 2d And 3d Computational Modeling Of Vacuum Arc Remelting: Capturing Rotating Arc Dynamics In Axisymmetric Simulations, Zilong Zhang, Elaheh Dorari, Ramesh Minisandram, Shakarjee Krishnamoorthi, Lang Yuan

Faculty Publications

Computational modeling of the Vacuum Arc Remelting (VAR) process has been developed to provide a deeper physical and metallurgical understanding and assist in the manufacture of defect-free ingots. While fully 3D, time-resolved arc models capture arc–melt interactions with high fidelity, their high computational cost makes 2D steady arc models still the preferred option in industrial applications. In this study, a multi-physics VAR model, which accounts for magnetohydrodynamics, heat transfer, fluid dynamics, and melting/solidification, was established in ANSYS Fluent for Alloy 718 in 3D with a rotating arc. It was validated against measured melt pool morphology. A new 2D axisymmetric oscillating …

Rapid Single-Cell Measurement Of Transient Transmembrane Water Flow Under Osmotic Gradient, Hong Jiang, Jinnawat Jongkhumkrong, Y. J. Chao, Qian Wang, Guiren Wang

Faculty Publications

Aquaporins (AQPs) are critical for transmembrane water transport in response to osmotic gradients, but their gating and regulatory mechanisms remain poorly understood. A central challenge is the lack of methods to measure water flow across AQPs from individual cells with the spatiotemporal resolution and sensitivity equivalent to patch-clamp recordings of ion fluxes—a limitation stemming from the electrically silent nature of water flow. Here, we present a novel optical technique—Flow-Induced Fluorescence Increase Velocimetry (FIFIV) based on Laser-Induced Fluorescence Photobleaching Anemometry (LIFPA)—that enables direct, real-time monitoring of cytoplasmic flow induced by transmembrane water transport under osmotic pressure gradients. Using small molecular fluorescent …

A New Highly Oxygen-Deficient And Cubic Pr3Zro8-Δ For Intermediate-Temperature Thermochemical Production Of Oxygen And Hydrogen, Jiaxin Lu, Yongliang Zhang, Luhong Chen, Yan Chen, Ke An, Yasser Shoukry, Xinfang Jin, Zhi-Hao Wang, Sai Mu, Kevin Huang

Faculty Publications

Two-step thermochemical cycles offer a clean route for hydrogen and oxygen production but are typically limited to high temperatures exceeding 1500 °C. Lowering operating temperatures would enable the use of alternative heat sources such as industrial waste heat. Here, we report Pr₃ZrO₈ as a new enabling material for efficient intermediate-temperature redox cycling, with thermal reduction at 900 °C in argon and steam oxidation at 400 °C. Pr₃ZrO₈ adopts a face-centered cubic structure similar to CeO₂ but exhibits significantly greater oxygen deficiency, achieving average oxygen and hydrogen fluxes of 331.7 and 70.3 µmol·g-1, respectively, …

Digital Twin Enabled Robot Collision Detection Using Time Series Forecasting, Fadi El Kalach, Mojaba A. Farahani, Philip Samaha, Thorsten Wuest, Ramy Harik

Faculty Publications

The advent of Industry 4.0 has reshaped modern manufacturing, driven by breakthroughs in cutting-edge technologies. A key example is the widespread deployment of sensors, which capture and transmit large volumes of operational data. This data surge has fueled the development of advanced Artificial Intelligence (AI) applications, enhancing manufacturing intelligence and efficiency. A key enabler of such intelligence is Time-Series Forecasting (TSF), which leverages historical data to predict future trends and events, thereby providing actionable insights for proactive decision-making. In parallel, Digital Twin (DT) technology has gained significant prominence due to its capacity for bidirectional communication with physical manufacturing systems, enabling …

Approaching Lower Bound Of Lattice Thermal Conductivity By Simultaneously Suppressing Diagonal And Off-Diagonal Phonon Contributions, Alejandro David Rodriguez, Riccardo Rurali, Changpeng Lin, Joshua Ojih, Mohammed Al-Fahdi, G. Jeffrey Snyder, Ming Hu

Faculty Publications

Pushing the intrinsic lattice thermal conductivity (LTC) in crystalline materials to lower bounds is crucial for fundamental materials research towards emerging technologies including thermoelectric energy conversion and thermal management in both hypersonic aircraft and next-generation turbine systems. However, in the ultralow LTC regime ( <  1 Wm-1K-1), the competition between propagative (particle-like) and coherent phonons—arising from off-diagonal components—poses a significant challenge in further reducing LTC. We perform quantitative analysis of 4700 materials using density functional theory (DFT), spanning all crystallographic groups, to elucidate the interplay between diagonal and off-diagonal phonon contributions. We identify a critical balance between these transport mechanisms, where intermediate phonon lifetimes ( ~ 1 ps) and slow group velocities ( ~ 1 km/s) collectively suppress both contributions, enabling ultralow LTC. Results from a large dataset of 31,058 structures by machine learning models strongly resemble the DFT trends of two-channel phonon transport. Leveraging these models, we screen 25,882 additional materials and confirm their properties with DFT, identifying 12 candidates with ultralow room-temperature LTC—including a record-low value of 0.132 Wm-1K-1. Our large-scale analysis reveals fundamental insights into dual-channel phonon transport, enabling rational design of ultralow LTC materials and accelerating the discovery of advanced phononic crystals with tailored thermal transport properties.

Intravascular Ultrasound Is More Accurate Than Angiography In Arteriovenous Vascular Access Lesions, James W. Decker, Dima Banihani, Curtis Honshideler, Saran Lotfollahzadeh, David Jasen Wu Wong, Alik Farber, Jeffrey J. Siracuse, Najia Idrees, Laura Dember, Mohammad Bader, Suvranu Ganguli, Vijaya Kolachalama, Tarek Shazly

Faculty Publications

Background:

Conventional angiography remains the standard diagnostic modality for arteriovenous (AV) access dysfunction in hemodialysis patients, but its geometric accuracy is limited. Intravascular ultrasound (IVUS) offers superior lesion detection, yet its absolute measurement accuracy remains uncertain. Using three-dimensional (3D) printed vascular conduits as reference standards, we assessed the accuracy of IVUS versus angiography, hypothesizing that complex conduit geometry, quantified by Gaussian curvature, would exacerbate angiographic error.

Methods

Clinically relevant AV access geometries were modeled with computer-aided design (CAD) and fabricated using 3D printing. Lumen diameters were measured by contrast angiography and IVUS and compared with CAD dimensions. Conduit geometry was …

Uav-Deployable Open-Source Sensor Nodes For Spatial And Temporal In Situ Water Quality Monitoring And Mapping, Matthew Burnett, Mohamed Abdelwahab, Joud N. Satme, Austin Downey, Gabriel Barahona Smith, Antonio Fonce, Jasim Imran

Faculty Publications

Cost efficient, spatially resolved water quality monitoring is essential for managing pollution and protecting aquatic ecosystems. This study presents a low-cost (approximately USD 200), open-source, unmanned aerial vehicle (UAV)-deployable in situ sensor node for real-time assessment of surface-water conditions. The system integrates sensors for pH, turbidity, temperature, and total dissolved solids (TDSs), with onboard data logging and real-time clock (RTC) synchronization. Bench validation of the sensor package yielded mean absolute percentage errors of 1.34% for pH, 5.23% for TDS, and 0.81% for temperature, and the device operated continuously for 42 h. Field deployment demonstrated its ability to resolve spatial gradients, …

Structural And Mechanical Properties Facilitate Shock Wave Damping By Helmet-Like Orbital Hoods In Snapping Shrimp, Mia Kazel, Rebekah E. Cammack, Alexandra C.N. Kingston, Ahmed A. Alshareef, Tarek Shazly, Daniel Isaac Speiser

Faculty Publications

Snapping shrimp damp the shock waves they produce and use as weapons with a helmet-like structure termed the orbital hood. Here, we ask how structural and material properties contribute to shock wave damping by orbital hoods in Alpheus heterochaelis. Using tensile mechanical testing, we find orbital hoods are approximately half as stiff as carapace and have twice the capacity for viscous energy dissipation. Microstructural features probably contribute to tissue-specific mechanical properties: the endocuticles of orbital hoods have almost twice as many lamellae as those of carapaces despite being half as thick, suggesting a mechanism for enhanced material mobility underlying …

Striation Characteristics In Atmospheric Pressure Ac-Driven Glow Discharge Operating In Monoatomic Gas, Ayuob K. Alwahaibi, Sang Hee Won, Tanvir Farouk

Faculty Publications

Striation in an atmospheric pressure AC-driven helium glow discharge operating at 25 kHz is experimentally characterized. Striation behavior is characterized by the visualization of discharges and voltage–current measurements over a range of plasma power. Voltage–current characteris tics reveal that striations form in the “normal” glow regime of operation, with a higher number of strata at low current, diminishing as dis charge current increases. The relative differences and similarities in the striation patterns during the positive and negative cycles are identified. High-speed imaging shows the striations to be traveling in time—“moving striations.” The spatial luminosity of the striations is tracked over …

Biologically-Inspired Multiscale Neuromorphic Architecture, Christian O'Reilly, Ramtin Zand

Publications

This white paper proposes a biologically-inspired multiscale neuromorphic architecture that bridges key gaps between artificial neural networks (ANNs), spiking neural networks (SNNs), and biological neural networks (BNNs). While SNNs offer promising energy efficiency, their broader adoption remains limited by suboptimal performance and the need for novel learning paradigms. To address these challenges, the proposed framework integrates structural and functional principles observed in the brain, including hierarchical organization, sparse and modular connectivity, predictive coding, and diverse neuronal dynamics.

The architecture operates across micro-, meso-, and macro-scales, incorporating neuron-level diversity (e.g., excitatory/inhibitory and principal/support cells), canonical microcircuits (CMCs), and large-scale hierarchical organization. …

Rapid Flood Inundation Mapping For Dam Failure And Operations, Shivakumar Balachandran, Parvaneh Nikrou, Ayman Mokhtar Nemnem, Reza Saleh Alipour, Sagy Cohen, Xingong Li, Erfan Goharian, Jasim Imran

Faculty Publications

Dam failure often causes catastrophic flooding. Although conventional hydrodynamic models are available for generating flood inundation maps, there is a need for low-complexity models that can provide rapid inundation estimates during emergency situations. Operational releases of large volumes may also lead to downstream flooding. This study utilizes two DEM-based models, OWP HAND-FIM and FLDPLN, to produce flood inundation maps for near-real-time operational applications. Three dam-related scenarios, including failures and controlled operations, are examined using these simplified models for the Fall River Dam in Kansas. Breach flows are estimated using empirical relationships, and discharge attenuation is calculated using an analytical approach. …

In-Situ Eval: A Modular Framework For Custom And Real-Time Rag Benchmarking, Ritvik Garimella, Kaushik Roy, Chathurangi Shyalika, Amit Sheth

Publications

Retrieval-Augmented Generation (RAG) has become the standard approach for integrating domain knowledge into Large Language Models (LLMs). However, fair comparison of RAG pipelines remains difficult: data preparation is often ad hoc, subsampling methods are opaque, parameters vary across implementations, and evaluation is fragmented. We present In-Situ Eval, a unified and reproducible framework that operationalizes the full RAG pipeline with configurable subsampling strategies and both RAG-specific and generic evaluation metrics. The platform supports two execution modes: an offline Dataset mode for evaluating precomputed outputs, and a live Retrieval mode for benchmarking RAG variants with state-of-the-art LLMs. Users can flexibly select datasets, …

When Disasters Trigger Cyber Vulnerabilities: Mapping Physical-Digital Interdependencies In Critical Infrastructure Systems, Dikshya Panta, Sicheng Wang, Aditya Sapkota, Prakash Ranganathan

Faculty Publications

Critical infrastructure (CI) systems such as power, water, communications, and emergency services are increasingly exposed to compound risks in which natural disasters and cyber incidents interact and amplify one another. Traditional risk assessments often isolate physical and digital threats, overlooking the cascading dependencies that emerge when operational stress, emergency reconfiguration, and adversarial exploitation coincide. This study conducts a 2019–2025 scoping review and introduces a Geographic Information System (GIS) driven six-stage disaster cyber compounding framework that characterizes, maps, and operationalizes compound risk across interdependent CI sectors. The framework integrates a common operating picture, analytic situational understanding, exposure mapping, threat-fingerprint encoding, detection …

Crystal Structures, Optical Behavior, And Magnetic Properties In Hydrated Lanthanide Iron Sulfates, Chole Jones, Silu Huang, Tyler L. Spano, Eric A. Gabilondo, Mohammed Al-Fahdi, Kara Trim, Mary Douglas, Rongying Jin, Andrew Miskowiec, P. Shiv Halasyamani, Ming Hu, Jie Ling

Faculty Publications

Single crystals of LnFe(SO₄)₃(H₂O)₂ (Ln = La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Ho, Er, Tm; compounds 1–11) and LnFe(SO₄)₃(H₂O) (Ln = Tm, Yb, Lu; compounds 12–14) were synthesized under hydrothermal conditions. Single-crystal X-ray diffraction (SCXRD) analysis revealed that the dihydrated compounds (1–11) crystallize in centrosymmetric (CS) structures, with the lanthanide ions adopting eight-coordinate geometries. In contrast, the monohydrated compounds (12–14) exhibit noncentrosymmetric (NCS) structures, where the lanthanide ions are seven-coordinated. Vibrating sample magnetometry (VSM) confirmed that compounds 2, …

Pdr-Stgcn: An Enhanced Stgcn With Multi-Scale Periodic Fusion And A Dynamic Relational Graph For Traffic Forecasting, Jie Hu, Bingbing Tang, Langsha Zhu, Yiting Li, Jianjun Hu, Guanci Yang

Faculty Publications

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and …