Digital Commons Network^™

Green Waste Biochar And Plant Growth-Promoting Bacteria Enhance Tomato Growth Under Combined Nutrient Deficiency And Salinity Stress, Soumaya Tounsi-Hammami, Munawwar Ali Khan, Mahra Alqemzi, Salama Ali Almehairi, Aneesa Rasheed Anwar

All Works

This study characterized a green-waste-derived biochar from date palms and ghaf trees and investigated its potential as a soil amendment with halotolerant Bacillus spp. to improve tomato seedling quality under dual stress of salinity and nutrient deficiency. Biochar was produced through pyrolysis at 450 °C and then characterized for yield, pH, electrical conductivity, proximate analysis, surface morphology, energy-dispersive X-ray spectroscopy, and heavy-metal content. Its effectiveness was tested both alone and in combination with a Bacillus sp. mix, using a completely randomized design with varying NPK fertilizer levels and saline irrigation. Tomato seedlings were evaluated 45 days after planting for various …

A Systematic Review Of Audio Deepfake Detection Techniques For Digital Investigation, Mahra Alnaqbi, Richard Adeyemi Ikuesan

All Works

Deepfake technology has been driven by advanced machine learning and revolutionized multimedia creation by synthesizing hyper-realistic content. It includes images, videos, and audio. While its creative applications in entertainment and accessibility are significant, the technology also poses critical risks, especially in fraud, disinformation, and identity theft. Audio deepfakes are a subset of this phenomenon that replicate human voices with enhanced precision, mimicking tone, accent, and subtle vocal nuances. This has raised concerns in security-sensitive domains like voice authentication and forensic investigations. This systematic literature review (SLR) adopts PRISMA guidelines to explore the state-of-the-art in audio deepfake detection. It examines existing …

Fig-Gan: Fundus Image Generation Via Deep Learning Based Generative Adversarial Network For Amd Disease Diagnosis, Kailasa Thrishul, Ahed Abugabah, Amina Salhi, Manel Ayadi, Mohamed M. Sithik, D. Jayaprakash, A. Ahilan

All Works

Globally, age-related macular degeneration (AMD) remains a main cause of irreversible vision loss. Recently, deep learning models have primarily focused on classifying fundus images for early detection of AMD progression. However, existing models rarely address the generation of future progression-aware fundus images, particularly when complete real longitudinal follow-up scans are unavailable. This limitation makes it difficult to track retinal changes over time and highlights the need for generative models capable of producing realistic drusen-level structural variations. To address these issues, a novel deep learning-based FIG-GAN model is to generate synthetic future fundus images from baseline inputs. Multi-Attention U-Net (MAU-Net) is …

Reimagining Sustainable Sulfur Concrete: A Systematic Review Of Sulfur, Waste Integration, And Durability, Vasilia Al Khaldi, Abdel Hamid I. Mourad, Maisa Elgamal

All Works

Sulfur concrete (SC) - a thermoplastic, waterless composite in which molten sulfur-based binder replaces Portland cement - is attracting growing research attention as a sustainable construction alternative capable of superior performance in chemically aggressive environments. This systematic literature review synthesises evidence from 127 sources (2000–2025), examining three interlinked dimensions: (i) the role of waste and industrial by-product materials as aggregate and filler substitutes; (ii) the influence of aggregate type, morphology, and grading on mechanical and microstructural performance; and (iii) durability under acid, saline, and freeze-thaw exposures. A transparent, PRISMA-aligned screening and classification protocol - here used as a structured review …

A Performance-Optimized V2v Task Offloading Framework For Real-Time Vehicular Communication, Tariq Qayyum, Asadullah Tariq, Ikbal Taleb, Mohamed Adel Serhani, Zouheir Trabelsi

All Works

As vehicular applications become increasingly complex, their computational demands often exceed the capabilities of individual vehicles. Vehicular Edge Computing (VEC) alleviates this limitation by enabling task delegation to nearby edge resources; however, high mobility, dynamic topology, and fluctuating vehicle density make real-time offloading decisions challenging. To address these issues, we propose a performance-optimized Vehicle-to-Vehicle (V2V) task offloading framework for dense and dynamic Vehicular Ad-hoc Networks (VANETs). The framework follows a two-stage design: (i) context-aware edge-node selection based on live topology capture via periodic beaconing, and (ii) cumulative score-based dynamic priority queuing at the selected edge node. The priority score jointly …

Smart Medical System Integrating Clinical Workflows For Robust Skin Cancer Detection Across Heterogeneous Pathologies, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

All Works

Skin cancer is among the most prevalent and life-threatening dermatological diseases worldwide, with melanoma responsible for a substantial proportion of skin cancer–related deaths due to delayed and unreliable diagnosis. Conventional clinical screening based on visual inspection and expert interpretation is inherently subjective and often affected by inter-observer variability, lesion heterogeneity, and imaging artifacts, highlighting the need for accurate and generalizable automated diagnostic systems. This study proposes a novel hybrid deep learning architecture for skin cancer classification that integrates an attention-guided autoencoder with a transformer-inspired global context modeling module, forming a unified and robust representation learning framework. The encoder–decoder structure is …

Kindergarten Teachers’ Perceptions Of Instructional Approaches For Teaching Sequencing Concepts: Worksheet, Hands-On, And Beebot-Integrated Lessons, Savannah P. Partin

Electronic Theses and Dissertations

The purpose of this qualitative multiple-case study was to explore kindergarten teachers’ perceptions of three instructional approaches for teaching sequencing concepts: worksheet-based instruction, hands-on manipulative instruction, and BeeBot-integrated instruction. Sequencing is a foundational early literacy skill that supports reading comprehension, story retelling, and understanding the order of events. Although sequencing is commonly taught through traditional instructional methods, limited research has examined how kindergarten teachers perceive the use of educational robotics as a tool for literacy instruction. The study was guided by constructivist theory, sociocultural theory, and developmental systems theory. Participants included six kindergarten teachers from a public elementary school in …

Natural Gas Hydrate Production And Co2 Storage Via Clathrate Hydrates: Challenges And Opportunities, Abdirahman Hassan Mohamed, Aliyu Adebayo Sulaimon, Haylay Tsegab, Bhajan Lal, Stefan Iglauer, Muhammad Ali

Research outputs 2022 to 2026

One of the most demanding environmental and economic challenges of this era is that of supplying the increasing global energy demand while reducing and neutralizing the CO2 footprint. In this regard, the provision of a secure and diversified energy supply and the sequestration of CO2 into geological formations are currently of great significance. However, the buoyant nature of CO2 under the temperature and pressure conditions of typical geological sites leads to the risk of CO2 leakage, and thus necessitates long-term monitoring. Therefore, the hydrate-based sequestration of CO2 beneath oceanic sediments has become a desirable alternative to conventional geologic sequestration in …

In-Process Large Language Model Requesting Through Knowledge Graphs For Interfacing With Manufacturing Digital Twins, Reiki Tanaya Karuka, Vinh Nguyen

Michigan Tech Publications

Digital twins (DTs) have potential to improve manufacturing processes by providing in-process monitoring and reducing trial-and-error while improving process prediction and performance. However, identifying anomalies or defects in a DT environment remains a very time intensive process that requires multidisciplinary expertise and significant manual effort in analyzing the system. Large language models (LLMs) offer potential capabilities to interpret system-level information and support user-driven querying, but their effectiveness is limited by hallucinations when reasoning over large and dynamic manufacturing datasets inherent to process monitoring DTs. Knowledge graphs (KGs) have emerged as efficient tools in the industrial domain to efficiently represent data …

Extending Geometric Acoustic Ray Tracing To Multi-Room Environments: A Case Study On Gunshot Sound Transmission Between Adjacent Rooms, Tyler Ton

Theses and Dissertations

Accurate localization of gunshots in multi-room building environments remains a challenging problem in acoustic forensics and public safety applications. Existing approaches model sound propagation within a single room, neglecting the transmission of acoustic energy through walls and other building materials. This thesis presents a study on modeling multi-room gunshot acoustic transmission, combining geometric ray tracing with structural acoustic transmission-loss modeling to generate impulse responses for two horizontally adjacent rooms separated by a shared wall, providing a foundation for future inter-room gunshot localization work. The proposed system uses GSound-SIR, a geometric acoustics engine, to simulate sound propagation in both of the …

A Data-Driven Koopman Framework For Station-Keeping On Near Rectilinear Halo Orbit, Anusha Sharma Malladi

Theses and Dissertations

Cislunar missions have gained significant attention in recent decades, motivating the need for efficient modeling and reliable control. In this work a Koopman operator based framework is developed for approximating the error dynamics around a reference Near Rectilinear Halo Orbit (NRHO) in the Earth-Moon Circular Restricted Three-Body Problem (CR3BP). A decoder free neural network is used to learn a lifted linear representation of the nonlinear CR3BP dynamics and a residual based approach is used to identify the corresponding control input matrix. The model is then implemented in a receding-horizon target point controller and compared with uncontrolled propagation and a State …

Comparative Performance Of Physiological Vital-Sign Forecasting Under Random And Patient-Wise Splitting Using Deep Learning, Lavanya Vasavi Chittem Reddy

Theses and Dissertations

Physiological vital-sign forecasting estimates future measurements based on recent temporal patterns and can support analysis of continuously recorded monitoring data. This study comparatively evaluated deep feedforward, recurrent, bidirectional recurrent, long short-term memory, and bidirectional long short-term memory architectures for one-step-ahead forecasting of peripheral oxygen saturation, heart rate, and pulse rate. Each model received consecutive observations of peripheral oxygen saturation, heart rate, pulse rate, respiratory rate, and age, while separate single-output models predicted the next value of the selected target.

Random and patient-wise data splitting were compared using identical input definitions, preprocessing procedures, model architectures, and training hyperparameters. The strongest architecture …

Developing Imitation Learning Policies By Exploring Diffusion Framework For Quadcopters, Likith Swamireddy

Theses and Dissertations

Imitation learning offers a promising approach in developing near-optimal onboard policies for nonlinear systems, by learning from computationally expensive trajectory planners during offline training. Existing State-of-the-art methods commonly learn deterministic mappings from observations to actions by training fast neural networks to mimic the expert demonstrations, which can limit their ability to capture the action distribution, when the policy is trained on multiple possible expert commands to reach the same target, and this may lead to unsafe scenarios in the obstacle environments. To address these challenges, this study explores Diffusion Models (DMs) as a feedback controller for quadcopters that learns the …

Study Of Fast Switching Metastable-State Photoacids And Controlled Drug Release From Polycaprolactone (Pcl) Vascular Scaffold, Rana Salman Abbood Abbood

Theses and Dissertations

Metastable-state photoacid (mPAH) has become a common tool for controlling and driving chemical processes with light. mPAHs with fast reverse reactions are desirable for precise temporal control or generating quick pulses of proton concentration. In this work, different approaches towards fast reversing mPAHs are studied. Experimental and computational results showed that stabilizing the charge–transfer intermediate is an effective way to increase the rate. A novel mPAH with a reverse reaction ≈500 times faster than the most widely used mPAH in methanol has been developed. Another water-soluble mPAH exhibited a reverse reaction with a rate constant of 7.8 s−1, the fastest …

Psychometric Evaluation And Cognitive Correlates Of The Tactile Mental Cutting Test: Investigating Reliability, Validity, Gender Differences In Spatial Ability, Executive Function And Social Emotional Competence Among Native American Elementary Students, Sujata Basnet

All Graduate Theses and Dissertations, Fall 2023 to Present

Spatial ability-the ability to understand, visualize, and mentally manipulate shapes and objects-is important for learning in science, technology, engineering, and mathematics (STEM). However, many traditional spatial ability tests rely heavily on drawings and other visual representations and have often been developed and evaluated with populations that do not adequately represent culturally diverse students. As a result, there is a need for assessment approaches that are developmentally appropriate, engaging, and potentially more inclusive for young learners. The Tactile Mental Cutting Test (TMCT) provides one such approach by allowing students to explore three-dimensional objects through touch and physical manipulation while solving spatial …

Switching Configurations For Reconfigurable Battery Packs: A Review, Kenneth Tetteh Akiti, Quoc Viet Phung, Iftekhar Ahmad, Daryoush Habibi

Research outputs 2022 to 2026

Conventional Battery Energy Storage Systems (BESS) are designed with fixed cell topologies, where the electrical connection between cells remains unchanged throughout their life. Although Battery Management Systems (BMS) and conventional cell-balancing techniques are employed to monitor and maintain performance, they often fail to address key challenges such as uneven cell aging, capacity mismatch, and fault isolation that arise especially during the second life of a battery pack. These limitations can result in reduced efficiency, reduced reliability, and shortened battery life. As large-scale battery packs become increasingly common in Electric Vehicles (EVs), smart grids, robotics, and renewable energy systems, the limitations …

Design Of Automatic Security System Based Internet Of Things At The Museum, Rahmat Ageng Yuwana, Nuryake Fajaryati, Ferry Jie

Elinvo (Electronics, Informatics, and Vocational Education)

This research aims to determine the procedures for developing an Internet of Things (IoT)-based Automatic Security System as a security device for museum objects and to test its feasibility based on user feedback in museums. The Design Thinking model used consists of five stages: Empathize (understanding the user’s perspective), Define (gathering information on the encountered problems), Ideate (generating innovative ideas), Prototype (creating a prototype of the obtained ideas, ensuring the functionality of each device feature, and validating the product through expert judgment for feedback and product revision), and Testing (evaluating the system’s feasibility using Usability standards based on ISO/IEC 9241-11, …

We All Read: Adapting An Early Literacy Program For Children With Developmental Differences And Their Families, Rachel Podl

Graduate School of Professional Psychology: Doctoral Papers and Masters Projects

Children with developmental differences face elevated risk for early literacy delays, while their caregivers experience significant difficulty accessing appropriate and supportive resources. This paper describes an adaptation of WeRead, an existing story-time and psychoeducation program through the University of Denver's Baby S.T.E.P.S. Lab, for children ages 0–7 with developmental differences and their caregivers. The program integrated a neurodiversity-affirming read-aloud, a strengths-based art activity, movement and music, caregiver psychoeducation on visual schedules, and collaboration with four community partners. The event drew approximately 65 attendees. A post-program survey, though limited by few responses, indicated that caregivers valued the program's accessibility, community partnerships, …

Lithium Titanate-Functionalized Mixed Matrix Polymer Membrane For High-Performance Osmotic Energy Conversion, Rockson Kwesi Tonnah, Yasaman Boroumand, Milad Razbin, Mostafa Vahdani, Amir Razmjou, Mohsen Asadnia

Research outputs 2022 to 2026

Reverse electrodialysis (RED) offers a promising route to harvest osmotic energy from salinity gradients, yet practical implementation is hindered by the lack of scalable, high-performance ion-selective membranes that combine efficient ion transport with mechanical robustness and environmental sustainability. Here, we report a mixed matrix membrane composed of polyethersulfone (PES), polyvinylpyrrolidone (PVP), and lithium titanium oxide (Li₂TiO₃, LTO) that addresses these challenges through biomimetic ion transport channels. The strategic integration of LTO nanoparticles introduces ion-exchange sites and oxygen-rich PES ether linkages to create preferential cation transport pathways that mimic biological ion channels. This mixed matrix design delivers over three-fold cation conduction, …

Ncf Sensor Coated With Cofe1.96la0.04o4/Lafeo3 Heterostructures For Room-Temperature Detection Of Liquefied Petroleum Gas Concentration, Ziqiang Liu, Fujian Tang, Yufang He, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Liquefied petroleum gas (LPG) is a highly flammable fuel widely used for cooking, heating and transportation, making real-time leak detection essential for preventing fire, explosion, and suffocation hazards. In this study, a room-temperature no-core fiber (NCF) sensor coated with CoFe_1.96La_0.04O₄/LaFeO₃ heterostructures was developed for LPG detection. The heterostructures were characterized using XRD, Raman spectroscopy, SEM, TEM, XPS, PL spectroscopy, and UV–vis spectroscopy. Experimental results showed that La incorporation and heterostructure formation refined the particles size, improved surface accessibility, and modulated the electronic structure and band gap, thereby enhancing LPG adsorption, carrier redistribution, and …

Mechanically Stabilized Porous Aerophilicity Unlocks Ampere-Rate Transport-Limited Co2 Electroreduction To Ethylene, Ahmed Badreldin, Lanxin Guo, Carter Racine, Gabriella Smith, Shaoqin Chen, Shengyao Wang, Baiyu Zhang, Mohammed Majdoub, Ahmed Al-Ostaz, Yun Hang Hu, Qingsheng Wang, Ying Li

Michigan Tech Publications

Gas-fed electrochemical CO2 reduction (CO2R) to multicarbon products is fundamentally constrained by mass-transport limitations that suppress selectivity and stability at industrially relevant current densities. Here, we demonstrate that these bottlenecks can be overcome by engineering a porous aerophilic polymer microenvironment around a simple CuxO electrocatalyst without altering the intrinsic catalyst. By systematically replacing conventional PFSA ionomers with mixed aerophilic binders and applying a mild thermal treatment, we construct mechanically stabilized, gas-accessible overlayers that regulate the local gas-liquid microenvironment balance at the three-phase boundary (TPB). This approach drives a dramatic enhancement in ethylene production, increasing the partial current density from 135 …

The Missouri Miner, November 10, 2016

The Missouri Miner Newspaper

No abstract provided.

Going With The Flow: Peptide Affinity Membranes For High-Throughput, High-Yield Purification Of Adenovirus And Lentiviral Vectors, Yuxuan Wu, Arianna Minzoni, Aishwarya Gangadhar, William Smith, Eduardo Barbieri, Irfan Ismail, Luke Remmler, Michael A. Daniele, Stefano Menegatti

Michigan Tech Publications

Membrane chromatography offers a compelling alternative to packed-bed resins for viral vector purification, as its open porosity and convective mass transport eliminate diffusive mass transfer limitations, enabling processing at substantially shorter residence times. Yet, the translation of affinity ligands from resins to membranes is challenged by the low density of reactive groups, which constrains functional ligand loading and limits binding capacity. Seeking to overcome this limitation, this study presents a method for functionalizing cellulose membranes with peptide affinity ligands and demonstrates its application to the purification of adenovirus serotype 5 (AdV5), VSV-G and Cocal pseudotyped lentiviral vectors (LVVs), and adeno-associated …

Immunomodulatory Role Of Muc5ac In Lung Cancer Progression, Julia G. Kim

Summer Research Program Abstracts

No abstract provided.

Prototyping Pinhole Collimators For Imaging Irradiated Nuclear Fuel, Narrie Loftus, Ashish Avachat, Joseph T. Graham, Ryan Fronk, Jordan Fox, Rachel Shaffer, Seth Kilby

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

Collimator design and fabrication is a costly and time-consuming process that does not lend itself to simplified testing. This manuscript presents an approach for the rapid prototyping of sub-millimeter pinhole collimators for nuclear fuel applications using low-melting-point, high-mass-density materials. Multiple collimators were fabricated to test collimator parameters and properties, including acceptance angle, aperture diameter, and magnification. The fabricated collimators were paired with a position-sensitive CdZnTe detector to determine the detector response convolved with the collimator. Preliminary analysis using these collimators showed that they were effective in collimating multi-energy gamma laboratory sources. Radiography was performed to confirm magnification and determine coarse …

A Review Of Gallium And Germanium Recovery From Industrial Solid Residues, Ernest V. Oteng, Marthias Silwamba, Lana Alagha, Alex Luyima

Mining Engineering Faculty Research & Creative Works

The global demand for critical elements such as gallium (Ga) and germanium (Ge), and the shift toward circular mining has accelerated the development of new extraction and recovery processing these metals from industrial solid residues. This transition is driven by a substantial surge in demand for advanced technologies and the depletion of primary ore. Consequently, industrial solid residues, particularly zinc processing residues, smelter slags, flue dusts, and coal fly ash, are emerging as viable alternative sources. These residues incorporate Ga and Ge through isomorphic substitution in phases such as ferrites, silicates, and glassy aluminosilicates, and are enriched by industrial processes, …

Spatialimaginer: Towards Adaptive Visual Imagination For Spatial Reasoning, Yian Li, Yang Jiao, Bin Zhu, Tianwen Qian, Shaoxiang Chen, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Spatial intelligence, which refers to the ability to reason about geometric and physical structure from visual observations, remains a core challenge for multimodal large language models. Despite promising performance, recent multimodal large language models (MLLMs) often exhibit fragile reasoning traces in spatial intelligence tasks that involve consistent spatial state recognition. We argue that these failures stem from a mismatch between the spatial recognition mechanism and the text-only reasoning behavior of these MLLMs. Effective spatial reasoning requires low-level geometric structure to be faithfully preserved and updated throughout the reasoning process, whereas textual representations tend to abstract away precisely these critical details. …

Semantic-Structural Decoupling: Disentangling Semantic Attention From Structural Bias In The Attention Manifold, Pengkun Jiao, Bin Zhu, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

The empirical success of attention mechanism in Multimodal Large Language Models (MLLMs) often obscures its inherent, subtle flaws. Specifically, MLLMs consistently exhibit disproportionate attention toward certain semantically uninformative visual tokens, a phenomenon termed "register" or "Visual Attention Sinks." While existing inference intervention methods attempt to identify these sink tokens and redistribute their attention weights, such approaches typically treat these tokens in isolation and suffer from computational inefficiency. Instead, we reframe this phenomenon as a generalized textual bias exerted over visual features that extends beyond isolated sink tokens. From this perspective, a pervasive structural bias leads to the dilution of the …

Electrochemical Behavior Of Passive Film On Electron Beam Powder Bed Fusion Fabricated Ti-6al-4v Alloy Under Uniaxial Tensile Stress, Hongyu Zheng, Mengdi Yan, Yun Bai, Qian Wang, Lai-Chang Zhang, Jin Xu, Hao Wang, Yingjie Ma, Shujun Li, Rui Yang

Research outputs 2022 to 2026

As medical titanium alloys inevitably undergo synergistic actions of stress and corrosion in human body, a comprehensive investigation of their relevant properties is deemed essential for ensuring their safe application. This work investigates the uniaxial tensile stress electrochemical behaviour of Ti-6Al-4V alloy fabricated by electron beam powder bed fusion under simulated implantation conditions. The results indicate that tensile stress facilitates rapid formation of a dense passive film, which contains a high proportion of high-valent oxides. As the passivation process proceeds, applied stress promotes more defects accumulation in the passive film, compromising its barrier function and reducing corrosion resistance. Under sustained …

Analyzing Developer Discussions On Eu And Us Privacy Legislation Compliance In Github Repositories, Georgia M. Kapitsaki, Maria Papoutsoglou, Christoph Treude, Ioanna Theophilou

Research Collection School Of Computing and Information Systems

Context: Privacy legislation has impacted the way software systems are developed, prompting practitioners to update their implementations. Specifically, the EU General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) have forced the community to focus on users’ data privacy. Objectives: Relying on the vast amount of data on developer issues available in GitHub repositories, our aim is to gather empirical evidence on the issues developers of Open Source Software discuss to comply with privacy legislation. Method: We examined such discussions by mining and analyzing 32,820 issues from GitHub repositories. We partially analyzed the dataset automatically to identify …