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Articles 274291 - 274320 of 5211491
Full-Text Articles in Entire DC Network
Optimizing Port Logistics Through Generative Ai: Revolutionizing Efficiency And Resilience In The Maritime Industry, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Gabriel Raicu
Optimizing Port Logistics Through Generative Ai: Revolutionizing Efficiency And Resilience In The Maritime Industry, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Gabriel Raicu
Engineering Management & Systems Engineering Faculty Publications
The maritime industry faces growing challenges in optimizing port logistics due to increasing trade volumes, environmental regulations, and supply chain disruptions. This comprehensive literature review examines the transformative role of artificial intelligence (AI), with particular focus on generative AI, in enhancing efficiency and resilience in port operations. Through systematic analysis of 23 peer-reviewed studies published between 2021-2025, this review synthesizes advancements in real-time data integration, machine learning, digital twins, IoT, and autonomous systems that collectively improve operational decision-making, risk management, and environmental sustainability. Key findings reveal that machine learning applications achieve 90% effectiveness ratings in operational optimization, while predictive analytics …
Harnessing The Power Of Gradient-Based Simulations For Multi-Objective Optimization In Particle Accelerators, Kishansingh Rajput, Malachi Schram, Auralee Edelen, Jonathan Colen, Armen Kasparian, Ryan Roussel, Adam Carpenter, He Zhang, Jay Benesch
Harnessing The Power Of Gradient-Based Simulations For Multi-Objective Optimization In Particle Accelerators, Kishansingh Rajput, Malachi Schram, Auralee Edelen, Jonathan Colen, Armen Kasparian, Ryan Roussel, Adam Carpenter, He Zhang, Jay Benesch
Data Science Faculty Publications
Particle accelerator operation requires simultaneous optimization of multiple objectives. Multi-objective optimization (MOO) is particularly challenging due to trade-offs between the objectives. Evolutionary algorithms, such as genetic algorithms (GAs), have been leveraged for many optimization problems, however, they do not apply to complex control problems by design. This paper demonstrates the power of differentiability for solving MOO problems in particle accelerators using a deep differentiable reinforcement learning (DDRL) algorithm. We compare the DDRL algorithm with model-free reinforcement learning (MFRL), GA, and Bayesian optimization (BO) for simultaneous optimization of heat load and trip rates in the continuous electron beam accelerator facility. The …
Incorporating Insulin Into Alginate-Chitosan 3d-Printed Scaffolds: A Comprehensive Study On Structure, Mechanics, And Biocompatibility For Cartilage Tissue Engineering, Afsaneh Jahani, Mohammad Sagdegh Nourbakhsh, Ali Moradi, Marzieh Mohammadi, Lobat Tayebi
Incorporating Insulin Into Alginate-Chitosan 3d-Printed Scaffolds: A Comprehensive Study On Structure, Mechanics, And Biocompatibility For Cartilage Tissue Engineering, Afsaneh Jahani, Mohammad Sagdegh Nourbakhsh, Ali Moradi, Marzieh Mohammadi, Lobat Tayebi
Electrical & Computer Engineering Faculty Publications
Osteoarthritis is a leading cause of disability worldwide, challenging current treatments to limited cartilage self-healing capacity. Cartilage tissue engineering (CTE) integrates cells, scaffolds, and signaling molecules, with Insulin being utilized as a differentiation biomolecule due to cost-effectiveness, dose-dependent influence on chondrogenesis, suitable biological activity, and ability to activate relevant receptors. Yet, administering differentiation biomolecules through conventional scaffolds poses a persistent challenge. Alginate (Alg) is commonly employed in CTE for its biocompatibility, though it lacks sufficient mechanical properties. Chitosan (Cs), while enhancing scaffold mechanical properties, but does not independently provide optimal support for chondrogenesis. While Alg-Cs scaffolds have garnered attention, challenges …
T3-Ciders: Fostering A Community Of Practice In Ci-And Data Enabled Cybersecurity Research Through A Train-The-Trainer Program, Wirawan Purwanto, Mohan Yang, Peng Jiang, Masha Sosonkina
T3-Ciders: Fostering A Community Of Practice In Ci-And Data Enabled Cybersecurity Research Through A Train-The-Trainer Program, Wirawan Purwanto, Mohan Yang, Peng Jiang, Masha Sosonkina
Electrical & Computer Engineering Faculty Publications
We present a training program named T³-CIDERS, the Train- The-Trainer approach to fostering cyberinfrastructure (CI)- and Data-Enabled Research in CyberSecurity. T³-CIDERS is a train-the-trainer program for advanced cyberinfrastructure (CI) skills that is designed to be synergistic with research, teaching, and learning activities in cybersecurity and cyber-related disciplines. The participants, termed 'future trainers' (FTs), are trained in effective instructional design and CI hands-on materials from DeapSECURE, developed in a previous CyberTraining program. T³-CIDERS aims to enhance cybersecurity research and education through broader adoption of advanced CI techniques such as artificial intelligence, big data, parallel programming, and platforms like high-performance computing (HPC) …
Obesity Prediction From Structural Mri Using Conformal Deep Learning With Uncertainty Quantification, W. Farzana, A. G. A. Temtam, B. Humud-Arboleda, L. Ma, M. Bean, F. Gerard Moeller, K. M. Iftekharuddin
Obesity Prediction From Structural Mri Using Conformal Deep Learning With Uncertainty Quantification, W. Farzana, A. G. A. Temtam, B. Humud-Arboleda, L. Ma, M. Bean, F. Gerard Moeller, K. M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Obesity arises from a neurobehavioral disorder in which the brain’s regulation of hunger and food intake is impaired, leading to an imbalance between energy consumption and expenditure. One key phenotype associated with obesity is body mass index (BMI). BMI is influenced by multiple causal pathways driven by behavioral, metabolic, and genetic factors. Traditional obesity prediction studies often rely on magnetic resonance imaging (MRI) voxel-based morphometry to correlate BMI with obesity-related clinical measurements and brain structure, predominantly gray matter volume (GMV). However, the altered brain regions are variable and widespread between studies, with some literature presenting contradictory results between BMI and …
Personalized Prediction Of Tumor Recurrence With Image-Guided Physics-Informed Computational Model In High-Grade Gliomas, Walia Farzana, Khan M. Iftekharuddin
Personalized Prediction Of Tumor Recurrence With Image-Guided Physics-Informed Computational Model In High-Grade Gliomas, Walia Farzana, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
High grade gliomas are infiltrating tumors characterized by their diffusive invasion and proliferative growth. Across and within patients heterogeneity of tumors makes it challenging to determine tumor spatial extent after surgical resection. Traditionally, tumor growth predictions after surgical resections rely on generalized models and population-based observations, which do not account for individual patient differences. To address this gap, we propose a personalized approach with image-guided computational model (digital twin) that incorporates physics-based modeling to predict tumor recurrence. Our digital twin involves an inverse modeling step, followed by a recurrence model that accounts for varying surgical effects. The physics-guided inverse model …
Data-Driven Gradient Optimization For Field Emission Management In A Superconducting Radio-Frequency Linac, S. Goldenberg, K. Ahammed, A. Carpenter, J. Li, R. Suleiman, C. Tennant
Data-Driven Gradient Optimization For Field Emission Management In A Superconducting Radio-Frequency Linac, S. Goldenberg, K. Ahammed, A. Carpenter, J. Li, R. Suleiman, C. Tennant
Electrical & Computer Engineering Faculty Publications
Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission-induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings.
Biomedical Mxene-Polymer Nanocomposites: Advancing Photothermal Therapy, Antibacterial Action, And Smart Drug Delivery: A Review, Ali Mohammad Amani, Lobat Tayebi, Ehsan Vafa, Mohammad Javad Azizli, Milad Abbasi, Ahmad Vaez, Hesam Kamyab, Daniel Simancas-Racines, Shreeshivadasan Chelliapan
Biomedical Mxene-Polymer Nanocomposites: Advancing Photothermal Therapy, Antibacterial Action, And Smart Drug Delivery: A Review, Ali Mohammad Amani, Lobat Tayebi, Ehsan Vafa, Mohammad Javad Azizli, Milad Abbasi, Ahmad Vaez, Hesam Kamyab, Daniel Simancas-Racines, Shreeshivadasan Chelliapan
Electrical & Computer Engineering Faculty Publications
MXenes are hydrophilic, conductive, tunable, and biocompatible two-dimensional ceramic materials prepared by etching the 'A' layer from their precursor MAX phases. Although MXenes show exceptional promise in photothermal therapy, biosensing, and regenerative medicine, they face challenges such as oxidative instability in physiological environments, limited drug-loading capacity, and unpredictable immune responses. To address these limitations, MXene/polymer nanocomposites incorporating both synthetic polymers (e.g., polyethylene glycol (PEG), polyvinyl alcohol (PVA), polyvinylpyrrolidone (PVP), and polylactic-co-glycolic acid (PLGA)) and natural biopolymers (e.g., cellulose nanofiber, gelatin, chitosan, hyaluronic acid, and soybean phospholipids) have been developed. These composites enhance functionality for biomedical applications such as photothermal cancer …
Adaptive Fusion Neural Networks For Sparse-Angle X-Ray 3d Reconstruction, Shaoyong Hong, Bo Yang, Yan Chen, Hao Quan, Shan Liu, Minyi Tang, Jiawei Tian
Adaptive Fusion Neural Networks For Sparse-Angle X-Ray 3d Reconstruction, Shaoyong Hong, Bo Yang, Yan Chen, Hao Quan, Shan Liu, Minyi Tang, Jiawei Tian
Electrical & Computer Engineering Faculty Publications
3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion, thereby improving …
The Impact Of Chromium Ion Implantation On Ald Lead Chalcogenide Thin Films, Haifeng Cong, Charlotte Poterie, Jean Francois Barbot, Helmut Baumgart
The Impact Of Chromium Ion Implantation On Ald Lead Chalcogenide Thin Films, Haifeng Cong, Charlotte Poterie, Jean Francois Barbot, Helmut Baumgart
Electrical & Computer Engineering Faculty Publications
Inherently the synthesis of semiconducting materials by Atomic Layer Deposition ALD produces only intrinsic undoped films which require the introduction of small amounts of impurities for doping to change them into extrinsic semiconductors. Apart from various in-situ diffusion doping techniques like delta doping during the ALD process, post deposition doping by ion implantation affords the best control of dose and doping profile. The present study investigates the impact of 180 keV Cr+ ion implantation on the properties of semiconducting ALD lead chalcogenide thin films to improve their thermoelectric figure of merit. The implantation was accomplished with 180 keV Chromium …
Energy-Aware Swarm Robotics In Smart Microgrids Using Quantum-Inspired Reinforcement Learning, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri
Energy-Aware Swarm Robotics In Smart Microgrids Using Quantum-Inspired Reinforcement Learning, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri
Electrical & Computer Engineering Faculty Publications
The integration of autonomous robots with intelligent electrical systems introduces complex energy management challenges, particularly as microgrids increasingly incorporate renewable energy sources and storage devices in widely distributed environments. This study proposes a quantum-inspired multi-agent reinforcement learning (QI-MARL) framework for energy-aware swarm coordination in smart microgrids. Each robot functions as an intelligent agent capable of performing multiple tasks within dynamic domestic and industrial environments while optimizing energy utilization. The quantum-inspired mechanism enhances adaptability by enabling probabilistic decision-making, allowing both robots and microgrid nodes to self-organize based on task demands, battery states, and real-time energy availability. Comparative experiments across 1500 grid-based …
Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri
Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri
Electrical & Computer Engineering Faculty Publications
Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions …
Jase 5.1 Cover, Kathy Nimmer
Jase 5.1 Cover, Kathy Nimmer
Journal of the Arts and Special Education
No abstract provided.
Jase 5.1 Table Of Contents
Journal of the Arts and Special Education
No abstract provided.
Celebrating Five Years Of The Journal Of The Arts And Special Education And Welcoming Our Inaugural Editorial Board, Jasmine Begeske, Kathleen M. Farrand
Celebrating Five Years Of The Journal Of The Arts And Special Education And Welcoming Our Inaugural Editorial Board, Jasmine Begeske, Kathleen M. Farrand
Journal of the Arts and Special Education
The co-editors of the Journal of the Arts and Special Education (JASE) provide a brief history and overview of recent accomplishments of the journal and describe priorities for the future. These priorities include regular publication cycles, special issues, expanded article types, engagement opportunities, and a recommitment to accessibility. The editorial concludes with guidance for future authors and an introduction to the inaugural editorial board.
Pollination, Maddie Epler
Protecting Our Land, Brendy Diaz
What’S The Problem?, Molly Freeman
Pumpkin, What Do You Need?, Christine Kim
Does The Boat Float?, Janae Byman
Pushing And Pulling, Mikkel Cunningham
Spread And Pollinate: How Animals Help Plants, Piper Ellis
Spread And Pollinate: How Animals Help Plants, Piper Ellis
2nd Grade
No abstract provided.
Changing Of The Leaves, Eden Jasicki
Seed Dispersal, Emma Cox
Will It Fly?, Elizabeth Grochocinski
Animals Helping Plants, Lilly Cadwallader
Hibernation And Migration, Brynne Eide
Exploring Light, Grace Pohodich
Pushes And Pulls, Gabby Duke