Digital Commons Network^™

A Review Of Full-Lifecycle Management Practices For Blue Mining Centered On Sustainable Development And Resource Efficiency, Hou Zhengmeng, Zhang Shengyou, Chen Qianjun, Sun Wei, Zhang Tian, Cai Nan, Huang Liangchao, Wang Qichen, Shi Tianle, Xu Xiaochuan

Coal Geology & Exploration

Against the backdrop of China’s goals of peak carbon dioxide emissions and carbon neutrality, along with the growing demand for critical minerals, this study presents a systematic review of domestic and international literature and engineering cases. Based on the Blue Mining concept proposed by Langefeld’s team of the Clausthal University of Technology and from the perspective of planning forward, this study organizes key technologies, treatment pathways, and essentials of full-lifecycle management used to transition mines from single-purpose stopes to multifunctional infrastructures. Centered on four guiding principles, i.e., energy, ergonomics, water resources, and circularity, this study reviews the synergetic planning process …

Influence Of Polyvinyl Alcohol Content On Starch-Based Bioplastic Reinforced With Chitosan And Microcrystalline Cellulose, Bagus Kharisma Putra, Andoko Andoko, Mochamad Viky Afandy, Muhammad Faizullah Pasha, Riduwan Prasetya

Chimica et Natura Acta

Starch-based bioplastics are promising sustainable alternatives to petroleum-based plastics; however, their limited mechanical strength and stability restrict broader packaging applications. This study investigates the influence of polyvinyl alcohol (PVA) content on the structural and functional properties of starch-based bioplastic films reinforced with chitosan and microcrystalline cellulose (MCC). Films prepared by solution casting with varying PVA compositions were characterized in terms of density, mechanical properties, thermal stability, biodegradation behavior, antibacterial activity, and structural features. Increasing PVA content produced denser film structures with improved tensile strength and thermal stability, while elongation at break reached its maximum at the intermediate formulation, indicating a …

How Fe(Ii)/2-Oxoglutarate Oxygenase Chooses Chlorination Over Hydroxylation: Electric Field-Driven Ligand Exchange Governs C-Cl Formation, Simahudeen Bathir Jaber Sathik Rifayee, Midhun George Thomas, Anandhu Krishnan, Kritika Gupta, Carter Davis, Tatyana Karabencheva-Christova, Christo Z. Christov

Michigan Tech Publications

Non-heme Fe(II)/2-oxoglutarate (2OG)-dependent halogenases catalyze highly selective C-H halogenation. BesD is a non-heme Fe(II)/2OG halogenase that performs regio- and stereoselective chlorination of the l-lysine (l-Lys) substrate. Understanding the mechanism by which halogenation is favored over canonical hydroxylation is essential for guiding enzyme engineering efforts aimed at converting hydroxylases into halogenases. Here, we combine molecular dynamics (MD) and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations to elucidate the origin of chlorination selectivity in BesD and variants derived from a homologous hydroxylase. Our results indicate that, although the initial inline Cl-Fe(III)-OH intermediate is inherently predisposed toward hydroxylation, it undergoes a two-step isomerization in …

Accelerating Wound Healing Rates With Cucurbita Pepo Leaf Extract Loaded Electrospun Poly(Methyl Methacrylate)/Halloysite/Chitosan/ Caco₃ Composite Nanofibers Through In Vitro And In Vivo Assessments, Samar A. Salim

Nanotechnology Research Centre

Cucurbita pepo leaf extract (CPE) was incorporated into electrospun poly(methyl methacrylate)/ halloysite/chitosan/ CaCO₃ (PMMA/Hal/CS/CaCO₃) composite nanofibers to develop a novel biomaterial for accelerating wound healing rates. The different nanofibrous scaffolds were successfully fabricated and characterized through scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). SEM analysis revealed uniform, smooth nanofibers, while FTIR and XRD confirmed the integration of CPE into nanofiber matrix, indicating an amorphous structure and effective dispersion of the extract. In vitro agar well-diffusion and antibiofilm assays revealed that the optimized formulation exhibited potent antimicrobial activity against wound-associated pathogens. The nanofibers composite based on …

Sentience, Sheetal Agrawal

Masters Theses

The increasing urgency for sustainable and adaptive systems has driven research toward embedding intelligence directly into materials rather than relying solely on external sensing and control systems. This thesis explores how smart material1 embedded systems can be designed to recognize and respond to environmental signatures, defined as measurable patterns such as temperature fluctuations and mechanical forces. Central to this investigation is the integration of shape memory alloys, particularly Nitinol, with geometry-based actuation mechanisms that amplify material behavior into functional system responses.

The central argument is that designing with smart materials is a design problem, not primarily a materials science problem …

Somatic Prosthetics For Planetary Resonance, Disha Dharesh Kumar R

Masters Theses

This thesis investigates how industrial design can transcend extractive technological paradigms in favor of relational interfaces that foster planetary attunement. Framing the climate crisis as a 'crisis of imagination', the research challenges the Western bifurcation of nature and culture by drawing on Indic cosmologies- which recognize stones, plants, and ecosystems as conscious at different levels and participants in a shared cosmic field. By synthesizing research in Biosemiotics, Quantum Information Pansycishm, and Neuroscience, the project redefines intelligence as a distributed, more-than-human phenomenon.

The research materializes as a speculative design artifact: a device that functions as a somatic prosthetic for planetary resonance. …

Characterizing Stiffness Dynamics Of Normal And Malignant Breast Spheroids Using Brillouin Microscopy, Razanne Rafat Zaghloul, Karlin Hilai, Chenjun Shi, Jitao Zhang

Medical Student Research Symposium

Background: Breast cancer progression and metastasis are closely linked to alterations in the mechanical properties of tumor cells and their microenvironment. Softer, more deformable cells are often associated with higher metastatic potential. While atomic force microscopy (AFM) is the current gold standard for mechanical characterization, it is limited to surface measurements and can damage 3D cultures. It remains unclear how the mechanical properties evolve over time in normal versus malignant spheroids. This study utilizes Brillouin light-scattering microscopy, a non-contact and label-free optical technique, to assess stiffness changes in normal and malignant breast epithelial spheroids over time. Understanding these mechanical signatures …

Towards The Integration Of Living Plants In Urban Design For Desert Regions \Fontsize1213\Selectfont\Textcolorblack(A Dynamic Shading Unit Inspired By Plant Movements), Amira Medhat Ibrahim Gouda

HBRC Journal

With the rapid urban expansion of new cities in hot desert climates, the challenge of designing open urban spaces and public squares becomes evident, particularly in providing thermal comfort. These spaces are exposed to intense sunlight and temperatures exceeding 40°C for extended periods, limiting their usability and increasing reliance on traditional, non sustainable shading methods. Addressing this issue, the research explores the potential of bio-dynamic shading units that utilize plants with self-moving responses such as nyctinastic and heliotropic movements as sustainable alternatives that adapt automatically to climatic changes without mechanical systems or energy consumption. The study assumes that integrating plants …

Characterization And Potential Suitability Of Some Egyptian Clays For Ceramic Building Applications, Marwa Askar, Medhat Sobhy El-Mahllawy, Mohamed Hamed Abdel-Aal, Ali Mohamed Ali Abd-Allah, Safia Gaber Al Menoufy

HBRC Journal

This study investigates the suitability of five Egyptian clay deposits—Wadi El-Natrun, Qasr El-Sagha, Kafr Homied, Fayed, and Suez Road—for ceramic building applications. Representative samples were characterized chemically and mineralogically, with grain size distribution and Atterberg limits determined to assess their industrial potential. Mineralogical analysis identified montmorillonite, illite, and kaolinite as the dominant clay minerals. Grain size and plasticity data, plotted on industrial diagrams, revealed that all samples are highly plastic clays with elevated clay fractions and minimal sand content. Such properties, while beneficial for molding, require modification to reduce excessive plasticity. The addition of non-plastic materials is recommended to enhance …

In Silico Molecular Docking Study Of Antidiabetic Bioactive Compounds From Brotowali (Tinospora Cordifolia) Targeting Glut4 In Type Ii Diabetes Mellitus, Gita Euaggelion Tarigan, Surya Dwira

Indonesian Journal of Medical Chemistry and Bioinformatics

Type 2 diabetes mellitus (T2DM) is a global metabolic disorder characterized by insulin resistance and impaired glucose uptake. Despite the availability of pharmacological therapies, limitations such as adverse effects and high costs highlight the need for alternative therapeutic candidates. Tinospora cordifolia has been widely reported to contain bioactive compounds with antidiabetic potential; however, comparative evaluation of their interaction with glucose transporter type 4 (GLUT4) remains limited.

This study aimed to identify the most promising bioactive compounds from Tinospora cordifolia targeting GLUT4 using an in silico molecular docking approach, followed by pharmacokinetic and toxicity (ADMET) prediction. Molecular docking was performed using …

From Frames To Strains: Analytically Modeling Inelastic Deformation Under Mapped Single-Site Impacts From High-Speed Footage To Internal State Variable Codes, Joby Milo Anthony

Doctoral Dissertations and Projects

This work adds insight to the physical phenomena of microstructural and stress strengthening of metal components by the inelastic deformation from Surface Mechanical Attrition Treatment (SMAT). Impact behaviors observed by high-speed footage of a Crank-Slider Mechanism (CSM) are examined in the context of analytically moving rigid bodies in spacetime and resolving kinematics upon impact until restitution via Finite Element Analysis (FEA). A Coupled Discrete-Finite Element Model (CDFEM) leverages Bammann plasticity, Horstemeyer damage and void nucleation, growth, and coalescence and Cho recrystallization Internal State Variable (ISV) models to show the localization of plastic strain and onset of recrystallization under any single …

Explainable Tree-Based Ensemble Models For Diabetes Prediction Using Shap, Maan Y Anad Alsaleem, Omar Shakir Hasan, Yahya Albugg

AUIQ Technical Engineering Science

Due to the generally unqualified nature of prediction data and the difficulty of interpreting predictions, predicting diabetes remains a significant hurdle in the adoption of machine learning within the medical domain. In this study, several tree-based machine learning techniques (LightGBM, XGBoost, CatBoost, and Gradient Boosting) were applied to predict diabetes using the 2015 BRFSS dataset, while two ensemble methods (soft voting and stacking) were employed to improve predictive accuracy. The performance analysis of the individual models and ensemble approaches indicates that CatBoost achieved the highest accuracy among the single classifiers (0.871), with an F1-score of 0.871 and a ROC–AUC of …

Towards A Methodology For Form Creativity Of Building Envelope To Enhance Thermal Performance (Biomimicry As A Tool For Form Creativity), Amal Ebrahim Ahmed Hassanin, Marwa Atef Abd-Elhady

Mansoura Engineering Journal

It has become necessary to achieve thermal comfort for users in buildings, which enhances human ability to work, create, or rest and enjoy. Since the building envelope serves as the link between the inside and outside of the building, forming the building envelope creatively improves and enhances thermal performance of buildings, thus achieving thermal comfort for users. Nature is the primary teacher and source of creativity for humans. Therefore, Biomimicry was chosen as a tool for creative formation, and the descriptive analytical approach was used by analyzing an example where nature was simulated in the formation of the external envelope …

Techno-Economic Assessment And Life Cycle Analysis Of Electrocatalytic Reduction Of Co2 To Ethanol., Omotolani Elizabeth Oduyebo

LSU Master's Theses

This study presents a techno-economic analysis (TEA) and life cycle assessment (LCA) of the electrocatalytic reduction of CO₂ to ethanol, a multi-carbon (C₂) product with significant market value. Prior TEA studies have relied on simplified lump-sum separation cost estimates, and prior LCA studies have rarely examined the combined effect of CO₂ source and electricity supply on carbon intensity gaps that this work addresses through process-simulation-grounded analysis. An Aspen Plus process simulation was developed for an anion-exchange membrane (AEM) electrolyzer system coupled with an extractive distillation separation train using ethylene glycol as the entrainer, achieving 99.9 wt.% ethanol purity …

Machine Learning And Formal Methods In Quantum Chemistry: Theory And Application, Ishna Satyarth

Computer Science and Engineering Theses and Dissertations

In recent years, the progress in inter-disciplinary application of machine learning and artificial intelligence (ML/AI) have truly transformed various fields, from weather forecasting and drug development to medical diagnostics, energy, and sustainability. Computational chemistry uses computational tools to model, predict, analyze, and explain chemical phenomena, while the Quantum chemistry specifically uses techniques based on quantum mechanics (as opposed to classical mechanics or empirical models). Quantum chemistry or Computational chemistry has also observed a momentum in application of ML techniques over the past decade significantly accelerating results and providing valuable insights into vast datasets, often surpassing traditional methods.

This dissertation explores …

Cfd Simulation Analyses Of The Blowdown Phase Of A Depressurized Loss Of Forced Cooling Accident In A Htgr, Keenan Kresl-Hotz

Nuclear Engineering ETDs

This research numerically investigates the helium-air mixing in HTGR containment cavities during the blowdown phase of a simulated DLOFC accident. The results of the performed CFD simulation analyses, using the commercial code STAR-CCM+, are compared with reported measurements from an experiment conducted at CCNY. The analyses investigate the effects of the RANS and LES turbulence models, numerical mesh refinement, time-step size, and flow rate and temperature of the injected hot helium into the scaled reactor cavity. Calculated parameters analyzed include the pressure and spatial distributions of temperature and oxygen concentration in the simulated reactor and steam generator cavities. CFD oxygen …

Assessing Uranyl-Specific Dnazymes And Dna-Aptamers For Uranium Binding In Environmentally Relevant Waters, Ashley R. Apodaca-Sparks

Civil Engineering ETDs

The legacy of uranium mining affects many communities in the western United States, resulting in elevated levels of uranium in surface water. The ability to quickly and accurately detect uranium on site in affected communities can lead to real-time water quality analysis that informs risk assessment and remediation efforts. The objective of this work is to advance the application of biosensing technologies for the measurement of uranium in waters affected by mining legacy. The third chapter of this thesis compares ANDalyze by AlpHa Instruments, a commercially available uranium biosensor, and inductively coupled plasma mass spectrometry (an EPA-verified method for uranium …

Technical Advancements In Single-Molecule Spectroscopy For High-Throughput Measurement Of Long-Time Dynamics, Quyen B. Le

Chemical and Biological Engineering ETDs

Single-molecule fluorescence spectroscopy is a powerful technique for resolving transient biomolecular dynamics and quantifying free energy landscapes, kinetics, and binding interactions. However, two fundamental limitations restrict its broader application: slow, labor-intensive data acquisition and the limited observation time imposed by fluorophore photobleaching. These limitations hinder its use in drug discovery and biomedical engineering applications that require both high-throughput and access to long-time dynamics. In this work, both challenges are addressed through technical advancements. First, a simple, generalizable approach is introduced to automate data acquisition, eliminating manual intervention during experiments. This increases the acquisition rate by more than an order of …

Nuclear Deterrence: Enhancing The Mission Through Smart Factory Predictive Solutions, Jarrod Matthew Ronquillo

Chemical and Biological Engineering ETDs

To meet the stewardship and modernization initiatives set by the Department of Energy new technologies must enter the manufacturing facilities within the nuclear deterrent complex. Predictive solutions begin with collecting data. An equipment health monitoring device was created to streamline data collection and organization for equipment and processes. A predictive and process performance dashboard was developed for a deionized water system. The dashboard used Western Electric statistical process rules and a machine learning regression algorithm to predict when the resistivity would fall out of specification. Lastly, a remaining useful life calculation was developed for all equipment related to nuclear deterrent …

Vegetated Canopy Heterogeneity Footprints In The Roughness Sublayer, Giulia Salmaso, Raul Bayoan Cal, Marc Calaf

Mechanical and Materials Engineering Faculty Publications and Presentations

Turbulent flows over horizontally homogeneous rough surfaces are categorized as rough‐wall boundary layer flows, while flows over homogeneous vegetated canopies are better described through a mixing‐layer analogy. At present, numerous studies have investigated canopy density as a transition mechanism between rough‐wall and mixing‐layer‐type flows. Yet, most considered canopies have been spatially homogeneous, with few exceptions investigating agricultural arrangements. However, most vegetated canopies are not homogeneously distributed, but instead contain gaps and spatial heterogeneities of different scales. In these cases, it remains unclear which are the dominant flow traits, and how spatial heterogeneity affects them. To help overcome these knowledge gaps, …

Visual Interpretability Of Multimodal Tissue Perfusion Classification Using Grad-Cam And Saliency Maps, Metehan Zorluoglu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate identification of the tissue perfusion phase from hand images can aid doctors in decision-making with non-invasive techniques. The present study proposes a multimodal deep learning model for classifying the tissue perfusion phase using infrared, thermal, and visible spectrum images of the human hand. The proposed model consists of various preprocessing techniques such as manipulation, homography alignments, and masking. The significant contribution of this thesis is the interpretability analysis of deep learning models, achieved through the analysis of saliency maps and the Gradient-weighted Class Activation Mapping (Grad-CAM) methods. The purpose of this method is to find out how the convolutional …

A Descriptive Analysis Of Plant Leaf Disease Detection Using Machine Learning And Deep Learning Models: A Systematic Review, Arzoo Chamoli, Anuj Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, …

Complex-Valued Convolutional Neural Network With Time-Frequency Representation For Electrocardiogram-Based Arrhythmia Detection, Kajeeth Kumar Gurusamy, Muthurajkumar Sannasy

Turkish Journal of Electrical Engineering and Computer Sciences

This research proposes an end-to-end procedure for arrhythmia detection based on electrocardiogram (ECG) signals using complex-valued convolutional neural network (CVCNN) incorporated with time-frequency representation. The proposed model leverages complex numbers to capture amplitude and phase information that enhances the ability of the model for detecting time-frequency variation in cardiac signals. First, signal preprocessing techniques---including normalization, wavelet denoising, and R-peak detection---are applied. Subsequently, the model extracts complex features from raw ECG data by employing the Hilbert transform to derive the analytic signal and the short-time Fourier transform (STFT) to generate a time–frequency representation. The proposed CVCNN framework effectively learns spatial-temporal features …

Cortical Bone Density And Thickness Assessment Of Intraradicular Sites In Adolescent Patients Using Cbct Imaging, Sara Endo

UNLV Theses, Dissertations, Professional Papers, and Capstones

Background: The indications for the use of cone beam computed tomography (CBCT) in orthodontics has grown since it was first introduced and dentists are discovering new ways that 3D images enhance diagnosis and treatment planning. Evaluating bone quality and quantity can be measured as bone density and thickness in CBCT imaging and is helpful for temporary anchorage device (TAD) placement in orthodontic treatment. TADs rely on primary stability and are commonly used in orthodontic treatment to increase anchorage and expand the limit that nonsurgical orthodontics can provide.

Objectives: This study aims to assess the bone thickness and density at different …

Machine Learning Assisted Development Of Al0.2cufemnni High Entropy Alloy Through Selective Laser Melting, Hareharen K Mr

Theses and Dissertations

High Entropy Alloys (HEAs) are an emerging class of advanced materials that have gained significant attention due to their exceptional mechanical strength, thermal stability, and structural performance. Unlike conventional alloys based on a single principal element, HEAs are composed of multiple elements in a near-equiatomic ratio. Despite these advantages, designing HEAs with tailored properties is difficult because of the enormous number of possible combinations and the limitations of traditional trial-and-error methods. To overcome these challenges, this study presents a machine learning (ML) based approach to accelerate the design and development of an HEA.

In this work, a newly designed composition, …

Accurate Diagnosis Of Diseases By A Novel Ai Pipeline Based On Feature Extraction, Feature Ranking, And Feature Selection From Medical Images, Tuğba Nur Bozkurt, Mehmet Emi̇n Yüksel

Turkish Journal of Electrical Engineering and Computer Sciences

The rapid growth of the global population has led to a substantial increase in the number of patients, while the availability of healthcare professionals has not expanded at a comparable rate. This imbalance highlights the urgent need for efficient and reliable computer-aided decision support systems that can reduce clinical workload while maintaining high diagnostic accuracy. In this study, a novel and systematically integrated artificial intelligence-based pipeline is proposed for medical image classification, combining statistical significance-driven feature ranking with evolutionary feature selection in a unified framework. The proposed pipeline consists of four sequential stages: feature extraction, ranking, selection, and classification. Features …

Machine Learning-Based Decision Support Models With Applications In Postsecondary Education, Marco Paolo Anglesio

Theses and Dissertations

This dissertation investigates the deployment of machine learning methodologies in an industrial engineering framework for the development of advanced decision support systems in the context of enrollment management. Drawing on techniques from educational data mining, the research addresses three key phases in the lifecycle of traditional and non-traditional students. First, it analyzes student retention using predictive classification models designed to identify individuals at elevated risk of attrition. Second, it employs temporal convolutional networks for time series forecasting, estimating aggregate enrollment levels over highly variable, finite planning horizons on the basis of partially observed data and using an asymmetric loss function. …

A Cradle To Grave Life Cycle Assessment Of Captured Landfill Methane Abatement Strategies, Joshua Caleb Richard

Theses and Dissertations

Landfill methane mitigation strategies are commonly evaluated using percentage-based leakage assumptions and linear scaling models. These approaches may misrepresent system performance at varying throughput levels and real-world operating scenarios. This thesis develops a system-level life cycle assessment framework that incorporates both fixed infrastructure losses and employs sensitivity analysis to more accurately characterize emissions from landfill gas management pathways. Three scenarios are evaluated: methane capture and flare, methane capture and combustion for electricity generation, and methane upgrading with pipeline injection as renewable natural gas. Results demonstrate that emissions models over- dependance on static factors may inadvertently lead to under-reporting and provide …

A Novel Machine-Learning Based Method For Resolving Secondary Structure Topology In Medium-Resolution Cryo-Em Density Maps, Bahareh Behkamal, Mohammad Parsa Etemadheravi, Ali Mahmoodjanloo, Amin Mansoori, Mahmoud Naghibzadeh, Kamal Al Nasr, Mohammad Reza Saberi

Computer Science Faculty Research

Medium-resolution cryo-electron microscopy (cryo-EM) density maps preserve substantial information about protein secondary-structure organization; however, accurately recovering the topology and connectivity of α-helices and β-strands remains challenging due to noise, structural heterogeneity, and the intrinsic resolution limitations that obscure residue-level detail. Topology determination is a key intermediate step toward building atomic protein models from medium-resolution cryo-EM density maps. It requires identifying the correct correspondence and orientation between secondary-structure elements (SSEs), i.e., α-helices and β-strands, predicted from the amino-acid sequence and those detected in the three dimensional (3D) density map. Despite significant advances in cryo-EM reconstruction and molecular modelling, this correspondence problem …

Agentic Scientific Machine Learning For Autonomous Model Discovery In Systems Pharmacology, Nazanin Ahmadi, George Karniadakis

Biology and Medicine Through Mathematics Conference

No abstract provided.