Digital Commons Network^™

Retracted: Capsule Network Model For Detecting Spoofing Attack In The Internet Of Medical Things (Iomt), Mohammad A. Alsharaiah, Mohammed Amin Almaiah, Mansour Obeidat, Rami Shehab

Iraqi Journal for Computer Science and Mathematics

The Internet of Medical Things (IoMT) has transformed healthcare delivery through real-time monitoring and data exchange. However, this integration of smart medical devices has also introduced critical cybersecurity threats, particularly spoofing attacks, which can compromise patient safety and system reliability. Conventional Intrusion Detection Systems (IDS) often fail to address IoMT-specific challenges such as class imbalance, computational constraints, and the need for real-time adaptability. This study proposes a Capsule Network (CapsNet)-based IDS that leverages spatial dependency modeling and hierarchical feature relationships to detect spoofing attacks in IoMT environments. Using the CICIoMT2024 dataset, we implemented a binary classification framework where spoofing instances …

Optimizing Beer Fermentation Through Intelligent Control, Azizbek Nodirbekovich Yusupbekov, Mirjalol Yusupov

Chemical Technology, Control and Management

This paper presents an intelligent control approach for optimizing the beer fermentation process using fuzzy logic and adaptive neuro-fuzzy inference systems. By incorporating multivariable inputs—temperature error and pH deviation—the proposed system effectively handles the nonlinear dynamics and biological variability inherent in fermentation. Simulation results demonstrate improved control accuracy, responsiveness, and robustness compared to conventional methods, making the approach suitable for integration in modern brewery automation systems.

Retracted: Automated Diagnosis Of Orthopedic Patients With Vertebral Column Disorders Using Advanced Mathematical Modeling, Chen Feng, Zhenhua Sun, Xinheng Dai, Hongli Wen

Iraqi Journal for Computer Science and Mathematics

Orthopedic disorders are multifactorial, making accurate diagnosis a significant challenge. This study introduces a novel method for classifying patients into three categories—normal, disc herniation, and spondylolisthesis—using biomechanical parameters derived from diagnostic datasets. To enhance classification accuracy, two meta-heuristic optimization algorithms—the Zebra Optimization Algorithm (ZOA) and Chaos Game Optimization (CGO)—are integrated with Adaptive Boosting (ADAC) and Light Gradient Boosting Machine (LGBM) classifiers. The experimental results reveal that ZOA significantly improves model performance, particularly in the ADAC classifier. The baseline ADAC model achieved a mean accuracy of 0.916, which increased to 0.952 after optimization with ZOA (referred to as the ADZO model). …

Deep Learning And Texture Analysis For Lung And Colon Cancer Predicting, Mohamed M. Neamah, Laith A. Al-Ani, Loay E. George

Iraqi Journal for Computer Science and Mathematics

Cancer remains a major cause of death worldwide, with lung and colon (LC) cancers presenting significant challenges to healthcare systems due to their high rates of occurrence and mortality. Early and precise diagnosis is essential for better patient outcomes. This research utilizes recent advances in deep learning (DL) and texture analysis (TA) to create a reliable predictive model for detecting LC cancer through histopathological images (HPI). A hybrid method is proposed that combines a gray-level co-occurrence matrix (GLCM) for extracting texture features with an adaptive modified EfficientNet B2 model (AM-EfficientNet B2) for deep feature extraction. These features are used to …

A Pilot Study On Tissue Deformation Using An Integrated Sensor–Actuator Blood Collection Setup In Aquaculture (Salmo Salar), Ishrak Siddiquee, Md Ebne Al Ashad, Ahmed Hasnain Jalal

Electrical and Computer Engineering Faculty Publications

This pilot study presents a sensor–actuator setup designed to evaluate tissue deformation in Atlantic Salmon (Salmo salar) during needle insertion. The system integrates three types of low-cost, commercially available force sensors to capture force profiles and identify biomechanical events associated with tissue layer transitions. Controlled insertions were performed on a deceased specimen, and the resulting force data were analyzed to quantify insertion dynamics and estimate tissue deformation. A simulation model based on the recorded force values was developed to calculate stress distribution and deformation, which ranged from 0.001 µm to 8.4 µm and from 0.3 N/m2 to 4.9 N/m2, respectively. …

Magnetic Nanoparticles Tethered With Zn–Dpa For The Removal Of Bacteria From Red Blood Cell Suspension, Tochukwu P. Okonkwo, Rajendra P. Gautam, Jacob B. Limburg, Breckin L. Forstrom, Bowen J. Houser, Aaron Rappleyea, Tyler P. Green, Joseph P. Talley, Alexander D. Daum, Stacey J. Smith, Karine Chesnel, William G. Pitt, Roger G. Harrison

Faculty Publications

Bacterial infections continue to drive the need for more effective and rapid methods for bacterial analysis. To address this, magnetic nanoparticles (MNPs) have emerged as promising tools, especially when their surfaces are modified with bacteria binders. The bis-zinc–dipicolylamine (Zn–DPA) complex is known for its broad affinity to bacteria. We have synthesized MNPs via a thermal decomposition method, encapsulated them in silica, modified their surface with Zn–DPA, and tested their ability to remove bacteria. The MNPs retain their superparamagnetic properties and crystallite structure after being encapsulated. The MNPs coated with silica and Zn–DPA effectively bind and remove both Gram-positive and Gram-negative …

Progressive Insights Into 3d Bioprinting For Corneal Tissue Restoration, Ilayda Namli, Deepak Gupta, Yogendra Pratap Singh, Pallab Datta, Muhammad Rizwan, Mehmet Baykara, Ibrahim T. Ozbolat

Michigan Tech Publications

The complex architecture of the cornea, characterized by specifically organized collagen fibrils and distinct cellular layers, poses significant challenges for traditional tissue engineering strategies to replicate its native function. 3D Bioprinting offers a promising solution by enabling the precise, layer-by-layer fabrication of corneal tissues, closely mimicking the essential characteristics needed for vision restoration and long-term graft success. This Review critically examines the key biomechanical, optical, and structural attributes of the cornea necessary for its effective engineering and accurate 3D bioprinting. It provides a comprehensive overview of different 3D bioprinting modalities utilized for corneal tissue engineering and offers insights into potential …

The Fate Of Intra- And Extracellular Antibiotic Resistance Genes Through Advanced Wastewater Treatment Process, Celestene Adrianne Sebag

Graduate Theses and Dissertations

More areas across the globe are becoming affected by water stress or water scarcity. The decrease in available freshwater due to overconsumption reveals the direct need to consider other sources for necessities such as drinking water or food production. Agricultural irrigation uses 70% of the world’s freshwater, making the focus on safe and reliable reclaimed water for agricultural reuse imperative. However, contaminants of emerging concern (CECs), such as antibiotics and their resulting antibiotic resistance genes (ARGs), can spread and affect human health if reclaimed water is not treated to the proper level. Antibiotics are widely used in human medicine and …

A Tailored Analog-To-Digital Converter Architecture For Optimal Performance In Troponin Cardiac Protein Detection, Karem Abdelgawad, Sameh O. Abdellatif

Electrical Engineering

In recent years, the detection of troponin cardiac proteins has emerged as a crucial component in the diagnosis and management of acute coronary syndromes. This paper presents a tailored analog-to digital converter(ADC) architecture specifically designed to enhance the performance of diagnostic systems for troponin detection. Utilizing a 180 nm CMOS process technology, the developed 8-bit Successive Approximation Register(SAR) ADC integrates key components, including a preamplifier, comparator, and a digital-to-analog converter(DAC), to optimize signal processing and ensure accurate data conversion from analog to digital formats. The ADC architecture is evaluated for its ability to achieve a substantial sampling rate of 10 …

Comprehensive Review Of In Vitro Approaches For Environmental Heavy Metal Exposure, Manas Warke, Madeline English, Camila Padilla, Lexie Gasco, Wendy Leisner, Rupali Datta, Smitha Rao

Michigan Tech Publications

Heavy metals are ubiquitous environmental pollutants, contaminating air, soil, and water via the erosion of natural deposits, as well as originating from anthropogenic sources, such as agriculture, industries, transportation, and landfills. The increasing utilization of heavy metals over the years, combined with the persistent nature of metals in the environment poses a direct threat to human and environment health. Although regulatory limits have been established for toxic metals, assessing the associated health risks using real-life exposure scenarios remains challenging. In this review, we summarize the development and use of in vitro models based two- and three-dimensional cell culture systems, focusing …

Labor Productivity Losses Across Construction Trades: A Machine Learning Approach, Tamima Elbashbishy, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Labor productivity is a major concern in the construction industry. Existing research on construction labor productivity (CLP) within specific trades has produced inconsistent findings due to differences in the factors analyzed. This lack of consistency makes it difficult to identify the most critical drivers of productivity losses across trades. To address this gap, this study adopts a cross-trade analytical approach to systematically identify and evaluate the inefficiencies impacting labor productivity in multiple construction trades. Specifically, the study (1) identified common organizational and project-level inefficiencies that influence labor performance; (2) conducted an expert-based survey to measure the frequency and perceived impact …

Multi-Fidelity Machine Learning Modeling For Aerodynamic Response Prediction Of Aerospace Vehicles, Ethan S. Jackman

Theses and Dissertations

Hypersonic vehicle design requires understanding complex aerodynamic phenomena across the full flight regime. This study presents a novel MF surrogate modeling methodology that enables the prediction the full field response across a vehicle’s surface. A Space-Filling Curve (SFC) is used to convert unstructured data into 1D vectors. The a Convolutional Autoencoder is used with transfer learning to reduce the dimensionality of the data. An Emulator-Embedded Neural Network (E2NN) combines multi-fidelity data for fast, accurate predictions. A benchmark analytical example and hypersonic application are used to evaluate the methodology. Using various numbers of samples and sampling strategies it is found that …

Single-Molecule Orientation And Localization Microscopy, Sophie Brasselet, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Single-molecule localization microscopy (SMLM) offers enhanced spatial resolution in optical microscopy, providing detailed insights into the spatial organization of proteins in cells at the nanoscale. Over the past decade, SMLM has progressively incorporated the capability to retrieve the orientations of single molecules using their polarized dipolar emission pattern. Here we explore recent advancements in single-molecule orientation and localization microscopy (SMOLM), which yields super-resolved images of molecular three-dimensional (3D) orientations, wobble and 3D positions. This advancement opens possibilities to explore the nanoscale organization and conformation of biological molecules as well as to monitor and design local 3D optical fields in nanophotonics. …

Ibi-Dt: A Novel Approach Combining Individualized Bayesian Inference And Decision Tree For Identifying Cancer Drivers And Their Interactions, Md Asad Rahman, Gregory F. Cooper, Jinying Zhao, Xinghua Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing …

Low-Resource Ecoacoustic Audio Classification, Enis Berk Coban

Dissertations, Theses, and Capstone Projects

Ecoacoustic monitoring via machine learning enables scalable analysis but is often constrained by labeled data scarcity, particularly in remote regions like the Arctic. This thesis confronts low-resource ecoacoustic audio classification by developing and evaluating complementary machine learning methodologies. We introduce EDANSA, the first publicly available, expert- labeled Arctic dataset of its kind, curated via novel active learning, alongside a baseline CNN. We systematically evaluate transfer learning, showing general audio embeddings effectively bootstrap classifiers for challenging Arctic sounds, significantly outperforming direct label mapping. Optimizing label utility, we investigate standard data augmentation and introduce novel audio data valuation via Shapley values, revealing …

Tissue Engineered Combinatorial Therapeutics For Spinal Cord Injury Repair, Inha Baek

Graduate Theses and Dissertations

Traumatic spinal cord injury (SCI) lead to temporary or permanent sensorimotor deficit due to the complex and multifaceted features of the injury site, making the treatments ineffective. Recent research emphasizes the potential of combinatorial therapeutics approaches combining different therapeutics, such as biomaterials and stem cell transplantation. In this context, nerve composite hydrogels, fabricated from decellularized sciatic nerve (dSN) and spinal cord (dSC) extracellular matrices (ECM), might be promising platforms due to their biocompatibility and ability to mimic native microenvironments. In this study, we developed and characterized nerve mimetic composite hydrogels embedded with human adipose-derived stem cells (hASCs) and investigated their …

The Application Of Vetiver Grass In Natural Disaster Mitigation And Environmental Protection In Vietnam: A Bibliometric Analysis And Literature Review, Truc Phan, Tan Nguyen, Thang Pham, Bich T. Luong, Huong Nguyen

Civil Engineering Faculty Publications

Vetiver grass (Vetiveria zizanioides or Chrysopogon zizanioides) is a versatile tropical plant widely recognized for its applications in environmental protection and natural disaster mitigation. In Vietnam, where natural disasters such as floods and landslides are frequent, particularly along highways, Vetiver grass has proven to be an effective bioengineering solution. This paper provides a comprehensive review of the applications and benefits of Vetiver grass in preventing soil erosion and stabilizing slopes along Vietnam’s transportation systems. A bibliometric analysis of 555 Scopus-indexed publications (2000 – 2024) was conducted using VOSviewer software to identify research trends, key themes, and knowledge gaps. The findings …

Communicating Wastewater-Based Surveillance Data To Drive Action, Kata Farkas, Devrim Kaya, Rasha Maal-Bared, Ahmad I. Al-Mustapha, Sarmila Tandukar, Ishi Keenum, Teemu Gunnar, Aaron Bivins, Matthew J Wade, Kyle Bibby, Tarja M. Pitkänen, Ananda Tiwari

Michigan Tech Publications

As exemplified during the COVID-19 pandemic, wastewater-based surveillance (WBS) can deliver near real-time, population-level pathogen data to guide public health action. Its impact, however, hinges on timely, transparent, and context-specific communication to stakeholders, including health authorities, policymakers, scientists, clinicians, and the public. This review examines current WBS communication practices, identifies persistent challenges, and proposes strategies to enhance relevance. Key challenges include data complexity, lack of standardised communication frameworks, ethical and privacy concerns, and variable stakeholder capabilities. The strategic use of digital platforms, such as dashboards, reports, press releases, and social media, alongside traditional media, can broaden reach and aid interpretation. …

Lung Injury Risk Curves From Behind Armor Blunt Trauma Using A Live Swine Model, Narayan Yoganandan, Lewis Somberg, Danielle Wilson, Alok Shah, Jared Michael Koser, Brian D. Stemper, Valeta Carol Chancey, Joseph Mcentire

Biomedical Engineering Faculty Research and Publications

Introduction

From structural, anatomical, and functional perspectives, components of the thoracoabdominal region are heterogeneous and physiologically and functionally different. Although their tolerances to injury are expected to be different, the current Roma Plastilina No. 1 clay penetration criterion for behind armor blunt trauma (BABT) is not specific to the body region. It is important to develop regional injury criteria to ensure its specificity. The objective of the study is to conduct impact tests on the lung region using a live animal model and develop injury risk curves using velocity and deflection metrics via parametric survival analysis.

Materials and Methods

Live …

Effect Of Short-Chain Polymer Binders On The Mechanical And Electrochemical Performance Of Silicon Anodes, Fei Sun, L. Zurita-Garcia, Dean R. Wheeler

Faculty Publications

Polymer binders are crucial components in providing both mechanical support and chemical stability to the structure of porous Li-ion electrodes. Particularly in silicon anodes, the active material undergoes substantial volume expansion of up to 275%. Due to the mechanical constraint of the current collector, these silicon materials tend to expand in the normal direction while exhibiting substantial particle rearrangement and plastic deformation. Conventional rigid binders such as polyacrylic acid (PAA) and polyimide (PI), while providing satisfactory initial capacity, do not eliminate diminished long-term performance. Our research attempts to develop binder formulations that can accommodate sufficient flexibility for the substantial volume …

A Comprehensive Review On The Recovery Of Lithium From Lithium-Ion Batteries And Spodumene, Asad Ali, Sadia Afrin, Abdul Hannan Asif, Yasir Arafat, Muhammad Rizwan Azhar

Research outputs 2022 to 2026

The growing demand for Lithium-Ion batteries (LIBs) for use within varied electronics and electric vehicles (EVs) has raised concerns about the sustainability of lithium extraction from natural deposits. This study provides a comprehensive comparison of lithium recovery through mining of spodumene deposits, and the recovery of lithium from used batteries via recycling, in terms of technological, economic and environmental impacts. Lithium recovered through mining and refining operations prompt significant land disruption and soil contamination and possess large ecological, water and carbon footprints. Contrastingly, lithium recovered through battery recycling undergoes successive heat and chemical treatments, leading to waste minimisation and a …

Reduced Order Models Of Hydrodynamically Interacting Flapping Wings, Jose Pabon

Dissertations

Fish schools exhibit a collective behavior and self-organization that is mediated by hydrodynamic interactions between individual fish. However, the long-time evolution of hydrodynamically interacting collectives is challenging to investigate due to the persistent influence of long-lived vortical structures, and the high-resolution requirements of direct numerical simulation at large Reynolds numbers. Reduced-order models have therefore played an important role in theoretical investigations of collectives of swimming bodies. The main results detailed herein are several new reduced-order models of swimmers that self-propel by flapping, i.e., by executing a prescribed periodic rigid body motion. The models are extensions of a discrete-time dynamical system …

Optimizing Hip And Knee Assistance For Walking And Sit-To-Stand Transitions: An Intrinsic Muscle Mechanics Based Predictive Approach, Neethan Ratnakumar

Dissertations

As the global population ages, the demand for wearable assistive technologies continues to rise, driven by their potential to enhance mobility and independence in older adults. Effectively designed controllers for lower-limb exoskeletons to assist sit-to-stand (STS) and walking are crucial for delivering efficient, safe, and comfortable assistance during daily activities. Traditionally, controller optimization involves biomechanical modeling and user-specific customization. Musculoskeletal simulations play a central role in this process by providing insights into human-exoskeleton interaction dynamics, thereby informing and refining control strategies.

This work presents a simulation-driven approach for developing exoskeleton controllers for walking and STS using two distinct methods: optimal …

Low-Cost Cutaneous Protoporphyrin Ix (Ppix) Detection (Cpd) Device For Follow-Up Monitoring Of Patients After Photodynamic Therapy, Md Asaduzzaman Rasel

Graduate Masters Theses

Background: Photodynamic Therapy (PDT) utilizes specific wavelengths of light to activate photosensitizing chemical compounds, known as photosensitizers, which induce the generation of cytotoxic reactive oxygen species (ROS) for the targeted destruction of cancer cells. Among various photosensitizers for PDT, Protoporphyrin IX (PpIX) is widely employed in oncology and dermatology due to its natural in situ generation via the metabolic conversion of 5- aminolevulinic acid (ALA), a non-phototoxic prodrug. Systemic administration of ALA after 3-6 hr drug delay leads to peak PpIX accumulation in tissues, facilitating therapeutic and diagnostic applications. However, PpIX can persist in the skin for 24–48 hours post-treatment, …

Multimodal Learning In Real-World Application: Enhancing Feature Representation And Training Strategies, Nana Lin

Graduate Doctoral Dissertations

Multimodal learning has emerged as a critical paradigm for developing intelligent systems that can understand and reason across diverse inputs such as images, text, and audio data. Despite significant advances, effective deployment of multimodal models in practice remains a challenging task. This dissertation explores how multimodal learning can be effectively applied to high-stakes, real-world scenarios, with a focus on enhancing feature representation and training efficiency. Specifically, this research investigates multimodal learning strategies in two key domains: healthcare and surveillance.

In the healthcare domain, we explored the data fusion and alignment approaches for cognitive decline diagnoses. First, we propose the LOVEMA …

Design And Development Of A Standalone Digital Holographic Microscope Employing Phase-Driven Reconstruction And Classification For Biomedical Imaging And Optical Diagnostics, Charlotte Kyeremah

Graduate Doctoral Dissertations

Access to advanced biomedical imaging technologies remains a significant challenge in resource-limited settings, especially for early disease detection and monitoring of diseases such as malaria, HIV, and other blood-borne diseases. Although point-of-care (POC) devices have gained popularity in global health, many rely on antibody-based tests, lateral flow strips, or optical readouts that often lack quantitative capabilities, sensitivity to early infections, or versatility in different diagnostic targets. In addition, these systems are typically dependent on disposable reagents or manual interpretation, which limits their effectiveness in remote areas. Digital Holographic Microscopy (DHM) presents a promising alternative as a label-free imaging method capable …

Piezoelectric Dc Generator Through Sequential In-Phase Polarization Variation, Hyun Soo Kim, Sunghoon Hur, In Woo Oh, Chulwan Lim, Huimin Qiao, Hyung Jin Choi, Min Seok Kim, Dae Sol Kong, Jong Hoon Jung, Joonchul Shin, Seung Hyub Baek, Jun Chen, Chong Yun Kang, Jeong Min Baik, Yu U. Wang, Shashank Priya, Seong H. Kim, Yunseok Kim, Hyung Suk Oh, Kyung Hoon Cho, Jungho Ryu, Hyun Cheol Song

Michigan Tech Publications

Energy harvesting has drawn growing interest as a reliable power source for IoT applications, with piezoelectric materials notable for their high sensitivity and straightforward integration. Their robust mechanical-electrical coupling also makes them ideal for harnessing environmental vibrations or mechanical motions. Still, standard piezoelectric harvesters inherently produce alternating current (AC), necessitating complex rectification steps and leading to substantial energy loss. This work introduces a direct current (DC) harvesting method that employs a novel in-phase polarization strategy, enabling a stable, continuous DC output. This approach surpasses prior attempts that offered only low or pulsed DC signals, achieving an open-circuit voltage of 33.44 …

Smart Mobility Technologies In Urban Areas Of Emerging Economies: A Bibliometric Analysis, Peter Mugisha, Rose Luke, Joash Mageto, Hossana Twinomurinzi

African Conference on Information Systems and Technology

Despite the adoption of smart mobility solutions in emerging economies, challenges such as traffic congestion, pollution and inadequate infrastructure still persist. This study analyses 540 scholarly articles published between 2003 and 2024 to evaluate how smart mobility technologies – such as Intelligent Transportation Systems (ITS), Internet of Things (IoT) and Artificial Intelligence (AI) – have been implemented in these regions. Data was retrieved from Scopus and Web of Science and analysed using Biblioshiny for bibliometric mapping and Atlas.ti for thematic analysis. The review identifies research trends and gaps, showing how ITS has improved transport management in cities like Nairobi, and …

Pressure And Force Dynamics In Artificial Muscle Actuators: A State-Space And Optimization-Based Approach, Mohammad Elzein

Dissertations and Theses

This two-part investigation explores the dynamic behavior of braided pneumatic actuators (BPAs) under bio-inspired pulse modulation, with the aim of improving their biomimetic force output and control. The first study examines the effect of pulse length and inter-pulse timing on BPA performance, revealing that force output is highly sensitive to the temporal structure of input pulses mirroring biological muscle behavior. Using dual-pulse actuation schemes, the results demonstrate that force responses exceed the additive contributions of individual pulses, with peak amplification occurring consistently at a 27 ms inter-pulse gap. Shorter pulse lengths (10–20 ms) yielded the highest normalized force increases, up …

Computational Fluid Dynamics (Cfd) Modeling For Bio-Inspired Aerodynamic Optimization In Autonomous Drones, Hyginus C.O. Unegbu, Danjuma Saleh Yawas

Makara Journal of Technology

This study explores the aerodynamic benefits of bio-inspired design modifications for autonomous drones using advanced Computational Fluid Dynamics (CFD) simulations. Four bio-inspired configurations—leading-edge serrations, winglets, riblet surfaces, and curved wings—were assessed and compared against a baseline drone model to evaluate their impact on aerodynamic performance. The results indicated that all bio-inspired designs significantly enhanced lift, reduced drag, and improved overall aerodynamic efficiency. The leading-edge serration configuration achieved the highest performance gains, with a 33.6% increase in maximum lift coefficient (CL) and a 29.5% improvement in lift-to-drag ratio (CL/CD), primarily due to delayed flow separation and reduced turbulence. Winglets minimized wingtip …