Engineering Commons^™

Modulation Of Prussian Blue Redox Signaling By Molecular Imprinting For Reagent-Free Electrochemical Detection Of Emtricitabine, Abdellatif Ait Lahcen, Gymama Slaughter

Center for Bioelectronics Publications

Reagent-free electrochemical sensors offer significant benefits for rapid, affordable point-of-care drug testing. In this study, we introduce a novel, reagent-free electrochemical sensor based on a molecularly imprinted polymer (MIP) specifically designed for the selective detection of Emtricitabine (FTC), a common antiretroviral used in HIV therapy. The sensor uses laser-induced graphene (LIG) electrodes, renowned for their high conductivity and porosity, ideal for electrochemical sensing. To enable reagent-free operation, the LIG surface was electrochemically coated with Prussian Blue, serving as a redox-active layer. Next, an MIP-PPy film was electropolymerized onto the Prussian Blue surface in the presence of FTC as a template, …

Ion-Imprinted Polymer-Based Sensors For Toxic Metal-Ion Detection In Water: Coordination Chemistry, Transduction Strategies, And Environmental Applications, Ghita Yammouri, Gymama Slaughter

Center for Bioelectronics Publications

Toxic metal contamination in aquatic environments remains a persistent threat to human health and ecosystems. Yet, the high cost, infrastructure demands, and centralized nature of conventional analytical methods constrain routine monitoring. Ion-imprinted polymers (IIPs), a subclass of molecularly imprinted polymers, have emerged as promising synthetic recognition materials for metal-ion sensing because they generate coordination-defined binding sites with high selectivity, chemical stability, low cost, and reusability. This review summarizes recent advances in Ion-imprinted polymer (IIP)-based sensing technologies for toxic metal-ion detection in water from 2016 to 2026. It examines the fundamental recognition chemistry of IIPs, major synthesis strategies used to generate …

Stiffening Of Soft Silicone Upon Deep Uv Treatment As Characterized Using Nanoindentation, Amanda Wilder, Zaria Booth, Caden Obermeyer, Saika Sharmin, Venkat Maruthamuthu

Mechanical & Aerospace Engineering Faculty Publications

Silicones are elastomers that have a wide variety of uses, including biomedical applications such as the coating of biomedical devices and as implants. Soft silicones with mechanical properties similar to those of biological tissues have particularly gained use as substrates for cell culture in mechanobiology studies. In this context, it would be desirable to be able to alter their surface mechanical properties with a relatively simple physical treatment. While deep ultraviolet (deep UV) or ultraviolet C (UV-C) treatment has been previously used as a surface treatment method for stiffer silicones formulations, the effect of this treatment on soft silicones relevant …

Senescent Fibroblasts In Aging And Pulmonary Fibrosis, Sara B. Palega, Aiwei Y. Borengasser, Yan Y. Sanders

Department of Biomedical and Translational Sciences Faculty Publications

Aging is a major risk factor for many chronic lung diseases, including Idiopathic Pulmonary Fibrosis (IPF), a fatal and incurable disease characterized by progressive fibrotic remodeling. Age-associated structural alterations, impaired regenerative capacity, and dysregulated cellular signaling collectively create a pro-fibrotic microenvironment. A central driver of this pathological shift is the accumulation of senescent cells, which undergo irreversible growth arrest and develop a robust pro-inflammatory senescence-associated secretory phenotype (SASP). Emerging evidence identifies senescent lung fibroblasts as critical mediators of IPF pathogenesis. These cells promote excessive extracellular matrix deposition, myofibroblast differentiation, and tissue stiffening, while simultaneously impairing epithelial regeneration. Together, these effects …

A Critical Perspective On The Society Of Environmental Toxicology And Chemistry's Adherence To Founding Principles— Opportunities For The Future, Barnett A. Rattner, Annegaaike Leopold, Carys L. Mitchelmore, Glenn W. Suter, Mark S. Johnson, Adriana C. Bejarano, Lawrence A. Kapustka, Niranjana Krishnan, Derek C. G. Muir, Beatrice O. Opeolu, Martha Georgina Orozco-Medina, April Reed, Bruce W. Vigon, Adam R. Wronski

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

The Society of Environmental Toxicology and Chemistry (SETAC) is a global organization whose mission is the advancement of environmental science and management through collaboration, leadership, communication and education. On SETAC's 45th anniversary, the following question was raised: Are the 1979 founding principles of SETAC, multidisciplinary approaches to solving environmental problems, multisector engagement and scientific objectivity, still useful, adequate and effective in fulfilling its mission? In a special session held at the 45th Annual Meeting in Fort Worth, Texas, United States, a critical evaluation of the founding principles was initiated by reviewing SETAC's history and ongoing activities, and recommendations were made …

First-Job Contract Review Cheat Sheet, Johanna Jones-Morris, Ashlee Martellacci

Teaching and Learning Resources

This cheat sheet helps first-time employees understand what to review before signing an employment contract. It highlights job duties, compensation, scheduling, employment terms, benefits, restrictive clauses, worker classification, and common red flags so that individuals can ask informed questions and recognize potentially unfair or unclear terms.

Carbon Nanotube/ Thermoplastic Polyurethane (Cnt/Tpu)-Based Triboelectric Nanogenerator With Self-Charge Pumping For Implantable Knee Load Sensing And Energy Harvesting, Osama Abdalla, Maryam Hosseini, Milad Azami, Amir Ameli, Emre Salman, Milutin Stanacevic, Ryan Willing, Shahrzad Towfighian

Mechanical Engineering Faculty Scholarship

Aseptic loosening and joint instability remain the primary modes of failure in total knee arthroplasty (TKA). To provide real-time biomechanical load monitoring, this study presents a triboelectric nanogenerator (TENG) utilizing a carbon nanotube/foamed thermoplastic polyurethane (CNT/TPU) composite dielectric layer paired with a Kapton tape between dual electrodes. By leveraging the triboelectric effect, the device functions simultaneously as an energy harvester and a self-powered load sensor. To overcome power density limitations, a self-charge pumping mechanism via a multi-stage voltage multiplier circuit (VMC) was integrated and evaluated under physiological cyclic loading (2100 N at 1 Hz). The system’s charge dynamics were further …

Nanosecond Pulsed Electric Fields For Extracellular Vesicle Engineering: From Electro-Exocytosis To Cargo Modulation, Art Neal, Teresa Graham, Mohamadmahdi Samandari, Arash Ghorbannia, Anca Dobrian, Stephen J. Beebe, Ruben M. L. Colunga-Biancatelli

Bioelectrics Publications

Extracellular vesicles (EVs), including small extracellular vesicles (sEVs) and medium/large extracellular vesicles (MVs), have emerged as promising therapeutic vectors and diagnostic biomarkers across various branches of biomedicine. However, the clinical translation of EV-based technologies remains constrained by persistent challenges in manufacturing: insufficient yield from primary cell sources, limited control over cargo composition, and the absence of scalable, standardized production platforms. Nanosecond pulsed electric fields (nsPEF) represent an emerging biophysical approach that can address several of these limitations. Unlike conventional electroporation, which targets the plasma membrane using microsecond-to-millisecond pulses, nsPEF delivers ultrashort (1–300 ns), high-amplitude (10–300 kV/cm) pulses that penetrate intracellularly …

Iuse: A Gamified Virtual Learning Platform For Connected Vehicle Applications To Enhance Undergraduate Transportation Education, Tianyu Shen, Di Yang, Kai Sun, Hong Yang, Kun Xie, Mansoureh Jeihani

Civil & Environmental Engineering Faculty Publications

Emerging transportation technologies are rapidly reshaping transportation systems and industry practice. However, most transportation undergraduate curricula still emphasize foundational topics such as geometric design, travel demand forecasting, pavements, and soil properties, typically delivered through lecture-centric instruction. While these subjects remain essential to the discipline, they do not fully reflect the pace of technological change or provide sufficient opportunities for experiential learning with modern tools and data. This gap limits students’ exposure to CV concepts and their ability to translate theory into practice.

Focusing on a key emerging technology, connected vehicles (CVs), this paper bridges the above gap by introducing a …

Comparative Assessment Of Energy And Emission Costs For Geothermal Heat Pumps And Fossil-Fuel Heating Systems Across U.S. Climatic Zones, Md Shahin Alam, Shima Afshar, Seyed Ali Arefifar, Mohammad Haq

Electrical & Computer Engineering Faculty Publications

In response to growing concerns over global warming and energy sustainability, transitioning from fossil-fuel-based heating systems to renewable alternatives is essential. This study evaluates the economic and environmental performance of geothermal heat pumps for building heating and compares it with conventional coal-fired boilers, natural-gas boilers, and diesel furnaces. Using the heating degree-day (HDD) method, heating energy demand was analyzed for four U.S. cities—Anchorage (AK), San Francisco (CA), Salt Lake City (UT), and Las Vegas (NV)—representing diverse climatic zones. The analysis integrates thermodynamic and economic parameters, including the coefficient of performance (COP = 2–5) and annual fuel-utilization efficiency (AFUE = 80–97%), …

Digital Twin Technologies For Battery Systems: Advancements, Applications, And Future Directions, Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, François Allard

Electrical & Computer Engineering Faculty Publications

The relationships among deep learning, edge computing, artificial intelligence (AI), and the most recent advancements in digital twin (DT) technology for battery energy storage systems are discussed in this paper. The study highlights the need for improved cloud-edge coordination, AI model development, and stronger cybersecurity features by demonstrating real-world applications of digital twin technology in electric vehicles (EVs), aircraft, and grid storage. It also described DT-based structures for fault detection, real-time monitoring, and optimization through standardization and battery management system (BMS) fusion. Because DT-based solutions for distributed energy resources (DERs) offer improved energy management systems, various studies have been conducted …

Gem-Can: A Real-World Dataset Of Can-Bus Attack Scenarios On An Autonomous Vehicle For Intrusion-Detection Research, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini, Tienake Phuapaiboon, Milad Khaleghi, Daniel Tobias

Electrical & Computer Engineering Faculty Publications

This paper presents GEM-CAN, a labelled Controller Area Network (CAN) dataset captured from an autonomous GEM e6 platform under both normal operation and controlled cyber-attack conditions.

The dataset contains ∼143 K frames comprising (i) ∼ nominal autonomous operation (∼100k messages), (ii) DoS floods using arbitration ID 0 × 00000000 (∼41 K messages), and (iii) data-tampering injections that reuse legitimate IDs for brake and steering-lock (∼1.3 K messages). Each record includes timestamp, arbitration ID (11/29-bit), DLC, eight payload bytes, and a Normal/Attack label. A companion metadata file enumerates attack windows, PCAN bus-load traces, bitrate, and test conditions. Data were collected with …

Interpretable Battery Soh Prediction: A Comparative Interpretability Framework For Multi-Architecture Ml Models, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

This work introduces a unified interpretability-efficiency framework for lithium-ion battery state of health (SOH) prediction using hybrid deep learning architectures. We comparatively analyze four hybrid models: CNN LSTM MultiHead, CNN Feature Extractor LSTM, DNN LSTM, and DNN BiLSTM to disentangle how network topology, feature composition, and computational design influence both predictive fidelity and physical interpretability. By integrating Monte Carlo Shapley (MC Shapley), background occlusion SHAP (BoSHAP), and ablation analysis, we quantify the contribution and robustness of five electrochemical feature groups: time, capacity, voltage, dQ/dV and peaks of dQ/dV from NASA battery dataset. The results reveal a consistent dominance of differential …

Generative Ai-Driven Optimization In Flexible And Reconfigurable Manufacturing Systems, Salah Hammedi, Hicham Chaoui, Lotfi Nabli

Electrical & Computer Engineering Faculty Publications

Flexible and Reconfigurable Manufacturing Systems (FRMSs) are essential for coping with variability in modern production environments; however, efficient scheduling and rapid reconfiguration remain challenging. This paper presents a hybrid optimization framework that integrates Colored Petri Net (CPN) modeling with Generative Artificial Intelligence (GenAI) to enhance scheduling performance and system adaptability. The CPN formalism ensures verifiable modeling of system dynamics, while a transformer-based generative model produces candidate scheduling and reconfiguration strategies. Simulation experiments were conducted under static, dynamic, and adaptive scenarios, including machine breakdowns and dynamic job arrivals. Performance was evaluated using makespan, mean flow time, machine utilization, and reconfiguration latency. …

Generalized Inverter Fault Detection Using Normalized Current Features And A Lightweight Bilstm Network, Mohammad Zamani Khaneghah, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Fault detection and diagnosis of three-phase inverter-fed motor drives is essential for ensuring system reliability, safety, and continuous operation in applications such as electric vehicles and industrial automation. This paper proposes a data-driven fault detection framework based on normalized current features and a lightweight bidirectional long short-term memory (BiLSTM) network which can be generalized to different motor power rating in the same controller system. A compact set of six time-domain features, consisting of the mean and root-mean-square (RMS) values of the phase currents, is extracted and normalized with respect to the average RMS value. This normalization effectively removes dependency on …

Statewide Corridor Evacuation Response And Re-Entry Behaviors In Florida During Hurricane Irma, Xin Wang, Yuan Zhu, Hong Yang, Kun Xie

Electrical & Computer Engineering Faculty Publications

Hurricane Irma stands as one of the most destructive tropical storms to make landfall in the United States, particularly impacting the State of Florida, where it prompted the largest evacuation in history with approximately 7 million residents. The profound consequences of mass evacuation underscore the critical need to understand travel behaviors during hurricane evacuation and the recovery process. This research analyzes statewide evacuation and re-entry patterns, leveraging diverse datasets, including TTMS data from main corridors and GIS data. A statewide corridor-based empirical analysis framework is constructed to characterize evacuation and re-entry response patterns using sensor-based traffic observations. The results show …

Affordable Course Content And Open Education Resources For Undergraduate Courses Teaching Fundamentals Of Wireless Communications And Networking, Dimitrie C. Popescu, Otilia Popescu

Electrical & Computer Engineering Faculty Publications

Wireless communication systems and networks along with the services they provide have become an essential component of the modern 21st century society, fueling job growth in the wireless industry and increasing the need for engineers specialized in wireless communication systems. As a consequence, over the past two decades, undergraduate courses teaching fundamentals of wireless communication systems and networks have become common in electrical and computer engineering and technology programs. At the same time, the number of textbooks dedicated to wireless systems and networks published by mainstream publishers has also grown, with availability in various formats and offerings and a significant …

Performance Assessment And Design Improvements For An Urban Coastal Detention Basin Under Intensifying Rainfall Extremes, Imiya Mudiyanselage Chathuranika, Agyare Asante, Faeghe Borhani, Xixi Wang, Mujde Erten-Unal, Dalya Ismael

Civil & Environmental Engineering Faculty Publications

Coastal urban areas are increasingly exposed to flooding driven by more frequent and intense rainfall events, rising sea levels, and expanding impervious surfaces. Norfolk, Virginia, a low-lying coastal city with aging stormwater infrastructure, faces heightened vulnerability to these hydrologic pressures. This study evaluates the hydraulic performance of an existing urban detention basin within the Edgewater–Larchmont catchment under 10-, 50-, and 100-year, 2-h design storms using the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM). Simulations were conducted for both pre- and post-development conditions to assess changes in peak discharge, storage capacity, and water level dynamics. Results show that urbanization, …

Short-Term Response Mechanisms Of Water Quantity And Quality Of Daihai Lake Under Temperature-Driven Changes, Hao Zhang, Xiaohong Shi, Xianhua Li, Junping Lu, Ruizhong Gao, Xixi Wang, Shuhao Zhang, Longmei Xie, Yu Liu

Civil & Environmental Engineering Faculty Publications

Temperature-driven mechanisms involving complex feedback and lag that affect the evolution of hydrological processes and ecological functions in cold- and arid-region lakes represent a core scientific issue in current hydrology and lake ecology research. In this study, based on month-scale temperature and environmental factor data from Daihai Lake in Inner Mongolia from January to December 2023, statistical methods (redundancy analysis, Tukey's test analysis, correlation analysis, structural equation modeling), time series analysis methods (dynamic time warping), and machine learning methods (random forest) were combined. A hierarchical and phased response framework was constructed that encompassed driver identification, path tracing, lag characterization, and …

Flood-Season Surface Water-Groundwater Interactions Across Distinct Geomorphic Units In A Yellow River Great Bend Tributary Basin, Yinlong Wang, Ruizhong Gao, Debin Jia, Xixi Wang, Tingxi Liu, Xiaomin Liu, Shiming Bai

Civil & Environmental Engineering Faculty Publications

Surface water-groundwater interactions are complex in the Great Bend region of the Yellow River. Clarifying water exchange and source differences among geomorphic units is important for understanding regional hydrological processes and improving water resources management. In this study, the Wulanmulun River Basin, a typical tributary of the Yellow River, was selected as the study area. A total of 90 water samples were collected, including 25 river water samples, 43 groundwater samples, and 22 precipitation samples. Gibbs diagrams, multivariate statistical analysis, PMF, and MixSIAR models were used to investigate the hydrochemical and isotopic characteristics, source contributions, and transformation relationships of surface …

Effect Of Graphene Oxide On The Upconversion Photoluminescence Behavior Of Er/Yb Co-Doped Pvdf-Go Composite Nanofibers, Saptasree Bose, Jack Ryan Summers, Bhupendra B. Srivastava, Karen Lozano, Victoria Padilla-Gainza

Mechanical Engineering Faculty Publications

Upconversion photoluminescence (UCPL) materials, particularly rare-earth (RE) doped nanoparticles, have garnered significant attention due to their ability to convert near-infrared (NIR) excitation into visible emission, offering benefits such as high photostability, long lifetimes, low autofluorescence, and deep tissue penetration. Among various platforms, polymer-based one-dimensional (1D) nanofibers with in-situ lanthanide doping remain relatively unexplored, despite their superior mechanical flexibility, processability, and potential for improved luminescence performance. In this study, we report the fabrication and UCPL quenching behavior of Er3+/Yb3+ co-doped polyvinylidene difluoride (PVDF) nanofibers incorporated with graphene oxide (GO), synthesized for the first time using the scalable Forcespinning® technique. PVDF, a …

Wideband Acoustic Immittance: Suitable Quantities For Assessing Middle-Ear Status, Kren Monrad Nørgaard, Jingxin Jessica Feng, Susan E. Voss

Engineering: Faculty Publications

This paper investigates the physical significance of ear-canal wave quantities—such as absorbance—that are commonly measured in wideband-acoustic-immittance tests and evaluates whether these quantities uniquely characterize middle-ear function. This is explored using simulations of lumped-element and transmission-line ear canals, along with measurements in three-dimensional-printed uniform and anatomical ear canals of varying geometry, terminated by the same middle-ear simulator. The results demonstrate the following points: (1) Wave quantities computed using estimated or geometrical cross-sectional areas do not uniquely characterize the middle ear because they describe the wave interaction between ear canal and middle ear and are therefore confounded by variations in ear-canal …

Explainable Ai-Driven Predictive Maintenance Curriculum For Smart Manufacturing, Abdullah Al Mamun, Murat Kuzlu, Katherine Smith, Vukica Jovanovic, Dalya Ismael, Adel El-Shahat, Angela Sicaja, Md. Hedayetul Islam Chy

Engineering Technology Faculty Publications

Nowadays, Industry 4.0 has transformed manufacturing industries into a data-rich system driven by IoT, automation, and Artificial Intelligence (AI). Within this context, Predictive Maintenance (PdM) provides a proactive strategy that leverages heterogeneous sensor data such as vibration, acoustic, electrical, and visual signals along with historical performance and advanced analytics to forecast equipment failures before they occur. Usually, AI-driven PdM (AI-PdM) enhances this capability by integrating AI-based sensor analytics to automate fault prediction and optimize system reliability. However, traditional AI-PdM often functions as a “black box,” providing limited interpretability of its decision-making process and posing challenges for trust, validation, and human …

Pafex: Compiler-Based Floating-Point Exception Detection For Gpu Kernels With Papispecific Software-Defined Events, Samin Islam, Shirley V. Moore, Christoph Q. Lauter

Graduate Student Papers (CS)

As high-performance computing becomes progressively heterogeneous, the dependence upon vendor specific tools for numerical correctness has become an impediment to portability. Although modern GPUs comply with the IEEE 754 standard, the lack of practical native hardware support to raise and handle exceptions (special values like ±∞ or NaN) is a well-known architectural limitation. To embed portable numerical correctness across heterogeneous systems, we propose an architecture agnostic prototype based on LLVM-compiler infrastructure. This framework detects floating-point exceptions in GPU kernels at the Intermediate Representation (IR) level, instrumenting both device code and host code, strictly complying with the 2019 IEEE 754 standard. …

Toward Neurosymbolic Reinforcement Learning Via Editable Specifications, Vedant Khandelwal, Hong Yung Yip, Amit Sheth

Publications

Reinforcement learning systems are commonly adapted to new settings by retraining or fine-tuning policies. This default is costly, difficult to audit, and poorly aligned with structured requirement changes such as revised safety rules, new operational constraints, or updated user preferences. We argue for an alternative abstraction: adaptation via edits to an external, human-readable specification that the agent consults at execution time. We propose conditioning decision-making on an editable knowledge graph encoding (i) rules capturing action applicability and high-level effects, (ii) hard constraints defining feasibility, and (iii) soft preferences shaping tradeoffs among feasible behaviors. Requirement changes become graph edits, not policy …

Estimating Sediment Properties Using A New Source Level Function For Wind-Driven Underwater Sound Derived From Long-Term Archival Data, S Bruce Martin, Martin Siderius

Electrical and Computer Engineering Faculty Publications and Presentations

Wind-driven breaking waves generate the background sound throughout the ocean. An accurate source level for wind-driven breaking waves is needed for estimating the ambient sound levels needed for sound exposure modeling, environmental assessments, and assessing the detection performance of sonars. Previous models applied a constant roll-off of sound levels at -16 dB/decade at all wind speeds, and these models' source levels were flat at frequencies below ∼1000 Hz due to a lack of measurements. Here, we analyzed 16 long-term archival datasets with limited anthropogenic sound sources to estimate the wind-driven source level down to 100 Hz. We estimated the site-specific …

Gaze Transition Entropy And Automation Trust In Multitasking Workspace, Yusuke Yamani, Austin Jackson, Tetsuya Sato, Feyishola Ashimi, Michael S. Politowicz, Eric T. Chancey, Makoto Itoh

Psychology Faculty Publications

Safe flight operation requires visual scanning across multiple displays in a cockpit, which collectively represent the state of the aircraft and supporting automation. Trust is a crucial factor that drives human-automation interaction, and recent work has suggested a relationship between an operator's visual attention and automation trust. One index that captures predictability of eye movements between different areas of interest is gaze transition entropy. The current work reanalyzed data from Sato et al., which examined eye movement patterns and trust in automation associated with the system monitoring task of the Multi-Attribute Task Battery. Results showed credible positive correlations between the …

Implications Of Quantum Computing For Enterprise Cybersecurity And Data Integrity, Manikantha Varaprasad Inakollu

Computer Science and Engineering Faculty Publications

Quantum computing represents a paradigm shift in computational capabilities that poses both unprecedented threats and opportunities for enterprise cybersecurity. This research examines the implications of quantum computing advancement on current cryptographic systems, data protection mechanisms, and organizational security frameworks. Through analysis of quantum computing developments from 2019-2024 and surveys of 280 cybersecurity professionals across various industries, this study identifies critical vulnerabilities in existing encryption standards and explores emerging quantum-resistant solutions. The findings reveal that approximately 78% of enterprises remain unprepared for quantum threats, with current RSA and ECC encryption systems facing potential compromise within the next 10-15 years. The research …

Nanofibrous Materials And Nanoparticles For Combating Antimicrobial Resistance: Synthesis, Integration, And Translational Perspectives, Rewati Raman Ujjwal, Ashish Dilip Sutar, Rahul Shukla, Gymama Slaughter

Center for Bioelectronics Publications

Antimicrobial resistance (AMR) is a major global health challenge driven by mechanisms such as biofilm formation, efflux pumps, and genetic mutations. Nanoparticulate and fibrous materials have emerged as promising strategies to overcome these limitations through multimodal antimicrobial action and controlled drug delivery. This review highlights recent advances in electrospun nanofibrous systems, including natural and synthetic polymer-based scaffolds, stimuli-responsive nanofibers, and functionalized patches. Nanoparticle-loaded nanofiber systems demonstrate enhanced performance, including bacterial eradication, sustained drug release, and significant biofilm disruption. Multifunctional systems combining antimicrobial, antioxidant, and immunomodulatory properties further show synergism. Emerging innovations, such as piezoelectric and smart sensing systems, enable self-powered …

Osmotic Dilators Vs. Foley Balloons: Bayesian Secondary Analyses Of The Dilafol Trial, Antonio F. Saad, Elenir B. C. Avritscher, Claudia Pedroza, Xiao Han, George R. Saade

Department of Obstetrics & Gynecology Faculty Publications

Background: Dilapan-S, a synthetic osmotic dilator used for pre-induction cervical ripening, has proven to be as effective as the Foley balloon in terms of vaginal versus cesarean delivery and has been associated with increased patient satisfaction. Nevertheless, its ability to lower cesarean rates and its cost-effectiveness remain poorly studied.

Objectives: To estimate the likelihood that synthetic osmotic dilators reduce cesarean deliveries and to evaluate their health system costs (2026 US$) and incremental cost-effectiveness compared to the Foley balloon during cervical ripening.

Study Design: Secondary Bayesian analyses were conducted using data from the single-center DILAFOL randomized controlled trial, which enrolled 419 …