Digital Commons Network^™

Cyberattacks On Port Infrastructures: A Decade Of Trends, Incidents, And Mitigation Strategies (2011-2024), Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

Port infrastructures are critical to global trade, handling over 80% of the world's cargo by volume. However, their increasing reliance on digital technologies has exposed them to a wide range of cyber threats. This paper provides a comprehensive analysis of cyberattacks targeting port infrastructures from 2011 to the present. We examine the types of attacks, geographical distribution, notable incidents, and underlying vulnerabilities. Additionally, we discuss mitigation strategies and future directions for enhancing cybersecurity in the maritime sector. Our findings highlight the urgent need for robust regulatory frameworks, advanced technological solutions, and collaborative efforts to safeguard critical port operations.

Predictive Maintenance In Naval Vessel Propulsion Systems For Enhanced Marine Operations Using A Bigmm-Hmm Framework With Divergence-Based Clustering, Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza

Engineering Management & Systems Engineering Faculty Publications

This study introduces a BiGMM-HMM Integration Framework designed to improve predictive maintenance strategies for naval vessel propulsion systems, addressing the need for efficient and reliable operation in marine engineering applications. The framework effectively manages multimodal sensor data by leveraging a unique combination of Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in a bidirectional architecture. It analyses the dynamic interactions between sensors and subsystems. Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. In contrast, Method 2 processes the entire dataset …

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …

Maritime Industry Cybersecurity Threats In 2025: Advanced Persistent Threats (Apts), Hacktivism And Vulnerabilities, Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Mihaela Hnatiuc, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

Background: The maritime industry, vital for global trade, faces escalating cyber threats in 2025. Critical port infrastructures are increasingly vulnerable due to rapid digitalization and the integration of IT and operational technology (OT) systems. Methods: Using 112 incidents from the Maritime Cyber Attack Database (MCAD, 2020-2025), we developed a novel quantitative risk assessment model based on a Threat-Vulnerability-Impact (T-V-I) framework, calibrated with MITRE ATT&CK techniques and validated against historical incidents. Results: Our analysis reveals a 150% rise in incidents, with OT compromise identified as the paramount threat (98/100 risk score). Ports in Poland and Taiwan face the …

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 …

A Governance-Centric Framework For Strengthening Healthcare Cybersecurity: A Systems Perspective, Sujatha Alla, Sai Gireesh Komaragiri, Teresa Duvall, Satluk Karahan, Nagesh Bheesetty, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Healthcare systems face unprecedented security and privacy challenges due to increasing digitization and interconnectedness. This paper provides a comprehensive analysis of these challenges by examining various cyberattacks, defensive mechanisms, and governance frameworks within modern healthcare infrastructure. The research systematically categorizes prevalent security threats, such as ransomware, insider threats, and data breaches, identifying vulnerabilities specific to healthcare systems. Furthermore, the study evaluates current defensive strategies, including encryption techniques, access control systems, and intrusion detection tools, assessing their effectiveness against complex cyber threats. A key focus is placed on governance structures and their role in cybersecurity resilience. The research explores how regulatory …

Enhancing Inquiry-Based Science Instruction: The Role Of Professional Learning Communities And Instructional Coaching For Elementary Science Teachers, Marc T. Sager, Karen Pierce, Jessica Murillo

Publications

Inquiry-based science instruction fosters critical thinking, problem-solving, and scientific literacy by engaging students in exploration, questioning, and reflective learning. However, implementing inquiry-based practices presents significant challenges for elementary teachers, who often balance multiple subjects and may feel less confident facilitating open-ended investigations. This study examines how school-based coaching and professional learning communities (PLCs) support teachers in adopting inquiry-based science instruction. Grounded in distributed cognition theory of learning, this research draws on qualitative data from classroom observations and artifacts from coaching sessions and PLC meetings. Using a multiple case study design, this study identifies four case studies to illustrate varying levels …

A Study Of User Experiences Of Pediatric Physicians With Electronic Health Record Systems: Encounters With Task Complexity And Efficiency Of User Task Flows, Roseanne Alhindi

CCAC Theses and Dissertations

way patient information is stored, managed, and accessed. This transition to Electronic Health Record (EHR) systems has enhanced the efficiency and accuracy of healthcare delivery by enabling quick access to patient records, reduction of errors, and facilitation of coordination among healthcare providers. In the EHR system, diverse tasks are performed for clinical processes and patient care. These tasks can be considered simple or complex, ranging from documenting patient visits and updating medical histories to ordering tests and managing prescriptions. Although EHR systems have become more prevalent in their use, there are noted challenges associated with the design of the system …

Empirical Assessment Of Cybersecurity Competencies Through Human-Generative Artificial Intelligence (Genai) Teaming, Dariusz Witko

CCAC Theses and Dissertations

The critical shortage of skilled cybersecurity professionals, with over 750,000 unfilled positions in the United States (U.S.), combined with rising practitioner burnout and the complexity of modern cyber threats, poses significant risks to national security. As Generative Artificial Intelligence (GenAI) emerges as a potential tool to support cybersecurity operations, its role in assisting human analysts with high-demand tasks offers both opportunities and challenges. While GenAI can improve efficiency, it also introduces adversarial risks if manipulated by malicious actors. This study investigated how human-GenAI collaboration can address these challenges, focusing on the fundamental cybersecurity knowledge, skills, and task completion required for …

The Internist's Post V.1 N.11, The University Of Texas Rio Grande Valley. School Of Medicine, Eunbee Cho, Osiris Fernandez, Hector D. Preciado, Sobia Memon, Diana Acosta Pozo, Corey Haines, Roque Mifuji Lira, Martha Solis, Cesar Gutierrez

School of Medicine Publications

No abstract provided.

Autonomic Dysfunction In Gastroesophageal Reflux Disease. The Neurogastro-Cardiac Axis: Friend Or Foe?, Sergio Sobrino-Cossio, Jorge Cossío-Aranda, Elymir S. Galvis-García, Gualberto Mateos-Pérez, Óscar Teramoto-Matsubara, Jesús A. González-Hermosillo, Juan C. Lopez Alvarenga, Mustafa Azizoglu, José M. Remes-Troche

School of Medicine Publications

Gastroesophageal reflux disease (GERD) is a complex and highly prevalent entity. Impaired gut-brain communication is associated with autonomic dysfunction. Modulation of the autonomic nervous system controls gastrointestinal functions. In GERD, a decrease in vagal tone (parasympathetic activity) and an increase in sympathetic activity with autonomic balance shifted towards the sympathetic system have been reported. Clinical questionnaires and non-invasive measurement of heart rate variability may be useful in patients with GERD to detect autonomic dysfunction. Restoration of parasympathetic system activity (mainly neuromodulation), with subsequent improvement of parasympathetic activity, will reduce the intensity of autonomic symptoms and GERD, improving quality of life. …

“Ringleader Who Has No Power”: A Qualitative Study Of Parent Uncertainty During A Nicu Admission Unrelated To Prematurity, Erin Rholl, Steven R. Leuthner, Kathryn J. Malin, Joanne Lagatta, Krisjon R. Olson

College of Nursing Faculty Research and Publications

Background

Parents of preterm infants experience stress related to uncertainty which impacts parent health outcomes. Less is known about the experience of parents whose infants are admitted for reasons other than prematurity. In this study we sought to describe (1) the experience of parents whose infants are admitted to a level IV NICU for reasons unrelated to prematurity in relation to uncertainty and (2) the effect of social and structural determinants on parent experience with uncertainty.

Methods

This single center qualitative study utilized interviews, questionnaires, and chart review at 2 time points: 2 weeks after admission and 3 months after …

Critical Care Advanced Practice Providers As Partners In Advancing Pediatric Intensive Care Unit Nursing Knowledge: A Needs Assessment., Mary Seidl, Meghan Roller, Whitney Seul, Laura Rose, Christine A. Schindler

College of Nursing Faculty Research and Publications

Background

Critical care advanced practice providers (APPs) are frontline providers in many pediatric intensive care units (PICUs). From 2020 to 2022, there was a significant downward shift in nursing experience level in the PICU at the authors’ institution. APPs may help provide additional in situ education.

Methods

Data were collected using role-specific needs assessment of PICU nursing staff and providers, assessing concept related to knowledge and job stress.

Results

Findings overwhelmingly demonstrated desire for more APP-led education. Overall, 27% of nurse respondents strongly agreed that current knowledge was adequate to provide safe and best care for their patients, and 47% …

Evaluation Of A Quality Cpr Coach Simulation-Based Competency Training: From Learning Outcomes To Patient Outcomes, Katie L. Mcdermott, Kari L. Rajzer-Wakeham, Jennifer M. Andres, Christine A. Schindler

College of Nursing Faculty Research and Publications

Background

Variability in cardiopulmonary resuscitation (CPR) quality and skill decay result in suboptimal application of CPR, which impacts patient outcomes following an arrest. Quality CPR Coach is a supplementary code team role that provides real time, verbal feedback directly to chest compressors to cognitively offload the team leader and ensure high-quality, goal-directed CPR delivery.

Methods

Design, implementation, and evaluation of a simulation-based Quality CPR Coach competency curriculum. Multi-modal assessment and evaluation methods were used.

Results

Statistically significant increase in confidence, perceived ability to verbalize Coach responsibilities, and knowledge. Participants demonstrated accurate Coach role performance across progressively scaffolded simulated compression event …

Investigating The Human Face Processing Network: Insights From Acquired Prosopagnosia And Hyperfamiliarity For Faces, Marie-Luise Kieseler

Dartmouth College Ph.D Dissertations

Human faces provide essential social cues that we interpret rapidly, allowing us to recognize identities, interpret emotions, and gauge familiarity. This thesis explores face processing disruptions in four case studies, each offering new insights into the brain’s face recognition mechanisms.

Chapter one presents Annie, a 28-year-old woman who acquired prosopagnosia (face blindness) following COVID-19. After recovering from the severe symptoms of the infection, Annie experienced significant difficulty in recognizing familiar faces and navigating familiar environments. Testing confirmed her impairment in face recognition but showed her preserved cognitive and basic visual processing abilities. Annie was still able to recognize objects and …

Application-Driven Materials Development Of Solid-State Conductive Composites Of Ultra-High Molecular Weight Polyethylene, Peder Solberg

Dartmouth College Ph.D Dissertations

Ultra-high molecular weight polyethylene (UHMWPE) is a linear long chain homopolymer valued for its toughness, wear resistance, and chemical inertness. In the medical field, these properties make it the material of choice for bearing surfaces despite recognized tradeoffs between wear resistance, toughness, and oxidation resistance. Electrically conductive composites of UHMWPE show promise to enable new types of smart and active load-bearing implants in a future of personalized medicine, if beneficial properties can be maintained with the addition of solid-state additives.

Clinical need-finding conducted through Dartmouth’s Training Program in Surgical Innovation revealed several potential applications for conductive, load-bearing polymers. These include: …

Design And Manufacturing Of Multifunctional Piezoelectric Composites, Huan Zhao

Dartmouth College Ph.D Dissertations

Piezoelectric materials possess a unique ability to convert mechanical energy into electrical signals and have broad industrial applications. However, monolithic piezoelectric materials such as piezoceramics and piezopolymers often suffer from inherent trade-offs among mechanical strength, elasticity, and durability. Piezoelectric composites, typically consisting of a polymer matrix with ceramic reinforcements, offer a solution by combining the advantages of each constituent. Nevertheless, maintaining stable mechanical performance in high-temperature environments remains a significant challenge as conventional polymer matrices usually experience structural degradation.

In this thesis, a novel piezoelectric composite is developed using a preceramic polymer (PCP) matrix with barium titanate (BTO) inclusions. PCPs …

Optical Methods For Time-Resolved Dosimetry And Oximetry Of Ultra-High Dose Rate Radiation Therapy, Megan A. Clark

Dartmouth College Ph.D Dissertations

Radiotherapy (RT) is a cornerstone method used to treat over 50% of the 2 million new cancer diagnoses each year in the United States. The success of RT directly relies on an optimal balance between maximizing the dose to the tumor while minimizing dose to surrounding normal tissues. Achieving this balance is often challenging due to underlying radiation transport and the presence of anatomical constraints that limit the beam delivery geometry. In turn, minimizing healthy tissue toxicity prevents use of a more aggressive tumor killing approach, and even in curative cases may decrease the patient’s quality of life due to …

Improving The Figure Of Merit In Iron-Vanadium-Aluminium Thermoelectric Alloy System Guided By Computational Modeling, Tao Fang

Dartmouth College Ph.D Dissertations

Thermoelectric materials can convert a thermal gradient to electricity. Currently, commercialized Bi₂Te₃ is brittle and expensive, which restricts the usage of this material. Compared to other thermoelectric materials, Fe₂VAl is cost-effective, stable and more robust mechanically. However, the figure of merit (zT) had been much lower than Bi₂Te₃. In this thesis, our main goal is to improve the figure of merit in Fe₂VAl system guided by computational modeling. We will start the discussion by presenting the Ge doped Fe₂VAl sample with record thermoelectric figure of merit value. …

Old, Flat, And Slow: Interior Greenland Snow And Ice Dynamics Revealed With Gnss, Derek James Pickell

Dartmouth College Ph.D Dissertations

The interior dry snow zone region of the Greenland Ice Sheet can no longer be confidently considered a reliably melt-free region, yet our ability to investigate and quantify the dynamic nature of this landscape is hindered by its remoteness. To overcome this challenge, this work describes a novel, low-cost, low-power GNSS (positioning) instrument that enables simultaneous measurements of (1) ice accumulation/ablation changes and (2) 3D ice flow. Deployed in a dense array, these GNSS instruments achieve similar performance to scientific-grade, commercial options (cm- to mm- precision), yet operate at < 60% of the power and < 34% of the hardware cost. Over a three year campaign, we analyze spatial and temporal patterns of accumulation in this region, using a technique called GNSS interferometric reflectometry (GNSS-IR). Observations with this technique show low bias and high precision relative to a validation study (-2.1 ± 2.9 cm), while we also demonstrate for the first time how GNSS-IR can reveal cm- to m- scale surface roughness, a critical yet often neglected measurement due to longstanding observational challenges. Patterns of accumulation show a spatial dependence linked to surface slope (~+0.7 mm w.e. km^-1 westward away from the divide) while surface roughness has a temporal dependence likely driven by wintertime high winds. Next, we combine GNSS-IR surface heights with the geodetic position time series of the antenna to derive surface elevation changes, which are compared to coincident ICESat-2 laser altimetry elevations. Observations of the surface show a millimeter-level relative bias and cm-level precision (-0.9 ± 3.8 cm) compared with the satellite altimeter, demonstrating for the first time that this technique is a viable ground-truthing method, while ICESat-2 performance has continued to exceed mission performance requirements. Finally, we examine station positioning through time to show a sensitivity to dynamic ice thinning and firn densification, two parameters than cannot be finely observed with space-based methods in this region. Together, these results provide the first ground‑based, high‑resolution picture of interior ice‑sheet change—capturing accumulation, roughness, surface elevation, and strain in one unified dataset. Our approach dramatically lowers logistical barriers, opening the interior of Greenland (and other remote regions) to sustained, quantitative monitoring at unprecedented spatiotemporal resolution.

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 …

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 …

Analyzing Visual Attention In Virtual Crime Scene Investigations Using Eye-Tracking And Vr: Insights For Cognitive Modeling, Wen-Chao Yang, Chih-Hung Shih, Jiajun Jiang, Sergio Pallas Enguita, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

Understanding human perceptual strategies in high-stakes environments, such as crime scene investigations, is essential for developing cognitive models that reflect expert decision-making. This study presents an immersive experimental framework that utilizes virtual reality (VR) and eye-tracking technologies to capture and analyze visual attention during simulated forensic tasks. A 360° panoramic crime scene, constructed using the Nikon KeyMission 360 camera, was integrated into a VR system with HTC Vive and Tobii Pro eye-tracking components. A total of 46 undergraduate students aged 19 to 24–23, from the National University of Singapore in Singapore and 23 from the Central Police University in Taiwan—participated …

Development Of 2d Microfluidics Surface With Low-Frequency Electric Fields For Cell Separation Applications, Madushan Wickramasinghe, Dharmakeerthi Nawarathna

Electrical & Computer Engineering Faculty Publications

Cell separation techniques are widely used in many biomedical and clinical applications for the development of screening, diagnosis and therapeutic tests. Current 3D microfluidics-based cell separation methods have limited applications in part due to low throughput and technical complexity. To address these critical needs, we have developed a 2D microfluidics surface which is the miniaturized version of a 3D microfluids cell separation device. Using low-frequency electric fields (1–10 Vpp and 1 kHz–20 MHz), we have first studied dielectrophoresis, AC electro-osmosis and capillary flow within a sessile drop, and finally utilized the results to develop the 2D cell separation surface. Our …

Flux-Weakening Control Methods For Permanent Magnet Synchronous Machines In Electric Vehicles At High Speed, Samer Alwaqfi, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Permanent magnet synchronous motors (PMSMs) are widely favored by manufacturers for use in electric vehicles (EVs) because of their many benefits, which include high power density at high speeds, ruggedness, potential for high efficiency, and reduced control complexity. However, since the Back Electromotive Force (EMF) increases proportionally with the motor’s rotational speed, it must be carefully controlled at high speeds. Flux-weakening (FW) control is required to avoid excessive electromagnetic flux beyond the power source and inverter’s voltage restrictions. This paper aims to compare various FW control strategies and analyze their effectiveness in maximizing the speed of PMSMs in EV applications …

Modeling Functional Connectivity With Learning And Memory In A Mouse Model Of Alzheimer’S Disease, Lindsay Fadel, Elizabeth Hipskind, Steen E Pedersen, Jonathan Romero, Caitlyn Ortiz, Eric Shin, Md Abul Hassan Samee, Robia G Pautler

Faculty, Staff and Students Publications

Introduction: Functional connectivity (FC) is a metric of how different brain regions interact with each other. Although there have been some studies correlating learning and memory with FC, there have not yet been, to date, studies that use machine learning (ML) to explain how FC changes can be used to explain behavior not only in healthy mice, but also in mouse models of Alzheimer's Disease (AD). Here, we investigated changes in FC and their relationship to learning and memory in a mouse model of AD across disease progression.

Methods: We assessed the APP/PS1 mouse model of AD and wild-type controls …

Enhancing The Research Enterprise At A Health Sciences University: A Call For An Institutional Paradigm Shift, Bryan G. Beutel, Taryn Bissing, Aaditya Patel, Nicolette Duong, Jan Talley

Student Publications

Background:

There exists a rapidly growing need for a universally robust research enterprise at health sciences universities because of recent changes in the medical education landscape. This study aimed to evaluate student perceptions of (and challenges conducting) research and to implement effective strategies to expose students to enhanced resources and training.

Materials and Methods:

An anonymous survey developed using participatory research methods was disseminated to all students at a graduate health sciences university in 2023. This measured their engagement in research, resource needs, and barriers. Descriptive statistics quantitively analyzed factors that influence student participation in research, perceived benefits, and obstacles …