Techno-Enviro-Economic Approach For Electrification Of Rural And Shrimp Farming Regional Development Of An Isolated Island In Indonesia By Utilizing Hybrid Renewable Energy Systems, Fiqih Akbar Wijaya, Mohammad Akita Indianto

Journal of Materials Exploration and Findings

One of the challenges in developing and archipelagic countries such as Indonesia is maintaining energy demand in rural and isolated areas due to difficulties in electrical distribution. For instance, in areas like Bawean Island, no additional electricity capacity has been introduced in the past year, leading to an unmet potential customer demand. One of the possible options is by utilizing Hybrid Renewable Energy Systems (HRES) that are integrated with existing fossil fuel-based energy systems to support the growing energy demand in the remote island. A case study in Bawean Island is conducted with the projected energy demand covers the energy …

Analysis Of Leak Testing Planning For Carbon Dioxide Piping Systems In The Development Of Carbon Capture, Utilization, And Storage Facilities In The Bintuni Basin, Devi Sentiani, Akhmad Herman Yuwono Professor

Journal of Materials Exploration and Findings

Carbon Capture, Utilization, and Storage (CCUS) technologies have become a key strategic response to global efforts to reduce CO₂ emissions. This study presents a leak testing strategy designed for a carbon dioxide (CO₂) piping system as part of a Carbon Capture, Utilization, and Storage (CCUS) project in the Bintuni Basin, West Papua, Indonesia. This study aims to ensure the system is properly prepared before commissioning by designing a leak test plan that is both practical and technical, based on ASME B31.3, ASME PCC-2, and API STD 520 Part 1. We conducted the process, which involves identifying the system boundaries, …

Functional Biopolymers Applied To Sustainable Technologies In The Environment And Healthcare, Fengjie He

UNLV Theses, Dissertations, Professional Papers, and Capstones

Biodegradable polymeric materials (biopolymers) are naturally derived materials known for their excellent biocompatibility, biodegradability, sustainability, and versatile chemical functionality. They have attracted increasing attention in various applications as alternative to synthetic materials ranging from food packaging to tissue engineering. Meanwhile, with intrinsic advantages, biopolymers have also emerged as promising materials in addressing contemporary challenges in both biomedical and environmental fields. Motivated by the significant potential of biopolymers and the growing need for sustainable materials, my research focuses on the design and engineering of biodegradable polymers with novel modification methods and application directions. In this work, two representative biopolymers are selected: …

Bio-Inspired Electroactive Polymer (Eap) Sensors For Surface And Canal Flow Sensing In Dynamic Environments, Nazanin Minaian

UNLV Theses, Dissertations, Professional Papers, and Capstones

Nature can often create some of the most efficient and elegant solutions to complex problems, and engineering stands to benefit greatly from these time-tested designs. One of the more sophisticated examples of this is the lateral line system in fish: a distributed network of superficial and canal neuromasts that enables aquatic species to detect fluid disturbances with remarkable precision. This dissertation leverages that biological framework to explore the potential of electroactive polymers (EAPs), aiming not just to replicate structure, but to emulate function.Two classes of EAPs form the basis of this work: electroactive plasticized polymer gels (EPPGs) and ionic polymer-metal …

Investigating Information Extraction And Language Models In Medical Domain Text Processing, Pouyan Nahed

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation demonstrates that carefully adapted language-model pipelines can transform unstructured clinical-trial and pharmacological prose into reliable, low-latency structured data. Four interconnected studies support this claim.Tri-AL platform. An open-source dashboard ingests all 440 k+ ClinicalTrials.gov records—including every historical revision—into a normalized schema and parses the 20 GB XML archive over 10x faster than a BeautifulSoup baseline, while exposing hooks for demographic analytics and supporting integration of user-defined modules. Clinical trial summarization. An encoder–decoder model is trained on 57k description–summary pairs to condense clinical trials into a few sentences. ROUGE evaluation shows a 20% improvement over the baseline, while graph-based evaluation …

The Dropbot: Design And Development Of A Custom Drone For Precision Water Drop Penetration Time (Wdpt) Testing, Mugundan Prakash

UNLV Theses, Dissertations, Professional Papers, and Capstones

Assessing the hydrophobic characteristics of soil is vital for understanding soil wettability or soil-water interactions, particularly in post-wildfire environments where water repellency can significantly impact ecosystem recovery, water infiltration, and erosion control. One key metric in soil wettability studies is the Water Drop Penetration Time (WDPT) test, which evaluates the hydrophobicity of soil and guides land treatment strategies. This thesis presents the design and development of DropBot, a custom-built drone platform engineered for the precise delivery and analysis of water droplets in WDPT tests.The DropBot, a custom drone, integrates a lightweight, 3D-printed frame with a self-leveling platform, enabling consistent droplet …

Ai-Driven Solutions For Electricity Fraud Detection: A Data-Centric Approach, Fatemeh Alimoradi, Zahra Alimoradi, S. Mohammadali Zanjani, Ghazanfar Shahgholian

NJF Intelligent Engineering Journal

Electricity fraud detection presents a significant challenge for power distribution companies, as non-technical losses resulting from fraudulent activities adversely affect revenue and operational efficiency. This study presents a machine learning-driven approach for accurately identifying fraudulent electricity consumption patterns. A comprehensive analysis of electricity usage data is conducted using multiple classification models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), a Stacking Model integrating SVM, KNN, Random Forest, and Gradient Boosting, as well as a Weighted Model leveraging confidence-based prediction adjustments. Model performance is evaluated using key metrics, including accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrices. The results indicate that …

Application Of Ann Artificial Network In Slope Behavior Evaluation Using Machine Learning Technique, Marziyeh Tourani, Hadi Bahadori

NJF Intelligent Engineering Journal

In geotechnical engineering, evaluating slope stability remains a significant challenge due to inherent soil variability and environmental uncertainty. This study explores the application of Artificial Neural Networks (ANN) in predicting the factor of safety (FoS) of slopes using dimensionless input variables. An initial dataset of 349 samples was refined to 259 validated cases after removing outliers and incomplete entries. The input parameters were transformed into nondimensional forms c / γh, (tan(⌀)) / (tan (β)), and p / γh based on fluid mechanics principles to improve generalization and reduce dimensional bias. A feedforward ANN model …

Sleep Disorder Diagnosis Using Eeg Signals And Lstm Deep Learning Method, Mohammad Reza Yousefi, Reza Rahimi

NJF Intelligent Engineering Journal

Diagnosing brain disorders, particularly sleep disorders, has emerged as a critical area of research in both basic sciences and engineering. Sleep disorders encompass a range of conditions, including excessive or insufficient sleep, frequent awakenings, difficulty falling asleep, and challenges in reaching deep sleep stages. A key application of sleep disorder diagnosis lies in supporting the medical community through the development of advanced algorithms and models capable of accurately identifying these disorders based on individual brain signals, such as those captured by electroencephalography (EEG). These sophisticated approaches facilitate the creation of personalized treatment programs, paving the way for more targeted and …

A Hybrid Deep Learning Approach For Lung Diseases Classification Using Vision Transformer And Densenet, Noor Abd Alrazak Shnain, Mohammed Abdulameer Aljanabi

NJF Intelligent Engineering Journal

Lung disorders, such as pneumonia, tuberculosis, and COVID-19, remain significant global health issues. Accurate and early detection is of paramount significance to ensure effective treatment and control. Conventional CNN models like DenseNet have shown superior performance in the classification of medical images, whereas Vision Transformers (ViTs) have recently gained momentum as formidable models to capture global context information. Each of these models, however, has limitations when operated standalone. This manuscript suggests an innovative hybrid deep learning framework that synergizes the benefits of both ViT and DenseNet to advance lung disease diagnosis using chest X-ray images. The hybrid model uses both …

A Meta-Analysis Of Historic Drinking Water Emergency Events Reported In News Articles, Victor Yu, Haiying Wang, Weiwei Mo

Faculty Publications

This study seeks to investigate historical drinking water contamination events that reported by news media to find the key determinants of their outcomes, including the duration and economic cost, and to quantitatively describe the characteristics of past contamination events. ProQuest Global Newsstream and Regional Business News were used to identify an initial list of events and supplemented by additional search on specific information including the response actions, economic cost, and more. These events were analyzed using descriptive statistics, correlation analysis, and regression modeling. Smaller community water systems were found to be more frequently impacted by contamination events. Use restrictions, except …

Classify Cancers Types Using Mirnas Profiles And Machine Learning, Wafaa Khazaal Shams

AUIQ Technical Engineering Science

Cancer classification is important for early diagnosis and effective treatment. This study proposes a hybrid framework that combines Fisher Discriminant analysis (FD) with Linear Support Vector Machine (LSVM) and Long Short-Term Memory (LSTM) networks. MicroRNAs (miRNAs) expression profiles for three types of cancer are used to evaluate the model. These are: lung- adenocarcinoma, ovary and pancreas cancers. The classification process is done using 5-fold cross valuation. Results show significant performance for LSTM compared to LSVM with and without features reduction. However, using FD analysis results in high accuracy reached 0.97and 0.96 for LSTM and LSVM respectively, using 50 miRNAs. The …

Architectural Optimization Of Façade Design Using Nano-Ceramic Films For Green Buildings In Baghdad’S Hot Climate, Lina M. Shaker, Wan Nor Roslam Wan Isahak, Ahmed Salih Mahdi

AUIQ Technical Engineering Science

This study aims to optimize the architectural integration and energy performance of nano-ceramic window films in a three-story Baghdad commercial building having an overall area of 2,000 m². In a 40% window-to-wall ratio (WWR) and climate-responsive orientation, films were selected for solar exposure; north façade overhanging high-transmittance movies, east and west vertical fins on medium-transmittance movies, and south horizontal louvers on low-transmittance movies. Architectural strategies for daylighting, comfort, and façade appearance with low solar heat gain (SHG) were considered. MATLAB 2022 simulations evaluated energy savings, daylight transmission, and solar rejection. The most effective film (either film, HD-IR05100) achieved …

Understanding And Evaluating Genomic Language Models, Aadit Kapoor

Master's Theses

Large Language Models (LLMs) have shown remarkable capabilities in interpreting complex patterns across various domains, yet their application to genomic data remains limited. We see great potential in leveraging LLMs for vital biological tasks, such as predicting transcription factor binding sites and identifying antibiotic-resistant genes. This emergent behavior positions LLMs as powerful tools for enhancing our understanding of intricate biological language. LLMs trained specifically on genomic data, such as DNA sequences, operate distinctly compared to those trained on natural language. This difference is evident not only in the architectural landscape of the models but also in the methodologies employed by …

High Temperature In Potato: Plant Responses And Adaptive Cultivation Strategies To Increase Production, Siska Rahmayani Gultom, Jajang Sauman Hamdani, Kusumiyati

Jurnal Kultivasi

Climate change, with global temperatures rising by 1.09°C from 1850–1900 to 2011–2020, threatens potato production, a critical staple crop, by exceeding the optimal temperature range of 15–20°C. This review synthesizes over 45 peer-reviewed studies published between 2015 and 2025 from Google Scholar and ScienceDirect to evaluate the physiological, morphological, and tuber quality responses of potatoes to high temperatures and to identify adaptive cultivation strategies for sustainable production. High temperatures reduce photosynthetic efficiency through chlorophyll degradation and stomatal closure, increase respiration, and divert photosynthates to vegetative growth, leading to 18–32% yield losses globally by the 2050s. Heat-tolerant varieties, such as Atlantic …

A New Lightweight Encryption Method Based On The Dna-Rc4 Substitution For Resource-Constrained Iot Devices, Athraa J. H. Witwit, Ahmed Fanfakh, Ali Kadhum Idrees

Iraqi Journal for Computer Science and Mathematics

Technology's impact on daily life necessitates increased data protection. Cryptographic systems improve security, ensuring confidentiality and legitimacy of Internet of Things systems. However, resource-constrained devices face challenges like memory, battery life, processing power, and small size. In light of this, lightweight cryptography (LWC) provides techniques specifically tailored to the constraints of resource-constrained Internet of Things devices. However, the presence of a fixed S-Box in some LWC algorithms, such as Advanced Encryption Standard, or the absence of one in others, such as Speck and Tiny Encryption Algorithm, renders them more susceptible to attacks. In this paper, we suggest a new lightweight …

Hybrid Organic–Inorganic Corrosion Inhibitors: Bridging Performance And Eco-Safety, Hasan M. Luaibi, Saja S. Al-Tweel

AUIQ Technical Engineering Science

Corrosion leads to global economic losses exceeding $2.5 trillion annually and accounts for up to 30% of infrastructure failures. Conventional corrosion inhibitors face limitations such as aquatic toxicity (LC50 <100 mg/L), moderate efficiencies (65–82%), and poor long-term durability (<500 hours). This review critically evaluates hybrid organic–inorganic corrosion inhibitors, focusing on their composition, mechanisms, performance metrics, and eco-safety. Hybrid systems comprising organic molecules integrated with inorganic matrices like sol–gel networks, nanocomposites, and layered double hydroxides (LDHs) demonstrate enhanced functionality through barrier effects, self-healing, and controlled release. These materials achieve inhibition efficiencies of 88–98.5%, outperforming conventional systems by 18–35%. Sol–gel coatings protect carbon steel at 92–98% efficiency in 3.5% NaCl, while LDHs sustain 94–97% protection over 1200–1500 hours. Nanocomposites reduce corrosion rates from 0.95 mm/year to 0.002–0.004 mm/year. Electrochemical techniques (EIS, PDP) show increased charge transfer resistance (500–2500 Ω·cm²) and coating resistance up to 10⁸ Ω·cm². Surface analysis reveals smoother films (Ra = 15–35 nm), higher contact angles (95–125°), and adhesion strengths of 12–18 MPa. Case studies report 90–96% protection in marine settings, 88–94% in acidic media, and over 92% under atmospheric conditions. Durability tests confirm >90% efficiency after 1000 hours, significantly higher than the <60% for conventional coatings. Overall, hybrid inhibitors offer a high-performance, eco-conscious alternative for corrosion control, combining efficiency, environmental safety, and scalability. Their superior metrics and adaptability position them as next-generation solutions for diverse industrial applications.

64th Annual Rocky Mountain Conference On Magnetic Resonance

Rocky Mountain Conference on Magnetic Resonance

Final program, abstracts, and information about the 64th annual meeting of the Rocky Mountain Conference on Magnetic Resonance. Held in the Snowbird Resort and Conference Center, Snowbird, Utah, August 3-7, 2025.

Transforming Movement Assessment In Physical Therapy Through Subaquatic Data Collection, Kaitlyn Mcdonald

Theses and Dissertations

This study explored the interest and perceived barriers to integrating subaquatic diagnostic technologies (SDTs) into hydrotherapy among licensed physical therapists. Seventeen semi-structured interviews were conducted using a mixed-methods design, with 15 interviews included in the final analysis. Quantitative data were analyzed using chi-square tests, while qualitative responses were coded thematically. Results indicated no statistically significant relationships between SDT interest and career stage or hydrotherapy access, though qualitative data highlighted concerns about cost, limited access, and usability. Despite mixed interest in adoption, participants identified several potential benefits of SDTs, including improved treatment tailoring, increased patient buy-in, and enhanced outcome monitoring. Functional …

Optical Trapping For The Study Of The Chemical And Physical Dynamics Of Single Particles, Yukai Ai

Theses and Dissertations

This dissertation explores the combined technique of optical trapping and Raman spectroscopy (OT-RS) to study the physical and chemical properties, as well as the dynamic surface changes, of single aerosol particles in air. The study covers a wide range of aerosol types, including bioaerosols (such as pollen, bacteria, and fungi), dust particles (such as black carbon, terrestrial, and extraterrestrial materials), and liquid droplets (such as sea-spray aerosols). The optical trapping system used in this work is based on the universal optical trap (UOT), which is capable of trapping and manipulating a wide variety of particle types. These include both transparent …

Synthesis And Ir Spectroscopic Study Of Complexes Of Selected 3d-Metals With 2-(Phenylmethyl)-1h-Benzimidazole, Gulnoza O. Babayeva, Gulbeka G. Mamatova, Daminbek A. Ziyatov, Lola Sh. Makhmudova, Shahlo Sh. Daminova

CHEMISTRY AND CHEMICAL ENGINEERING

The aim of this study was the synthesis and structural investigation of coordination compounds of selected 3d-metals with 2-(phenylmethyl)-1H-benzimidazole (FAB) using IR spectroscopy. Complexes of copper(II), nickel(II), cobalt(II), and zinc(II) with the general formula [M(FAB)₂Cl₂], as well as a complex [Co(FAB)₂(ac)₂], were synthesized. IR spectroscopic analysis revealed that FAB coordinates to metal ions through the nitrogen atom of the benzimidazole ring. Shifts in the ν(C=N), ν(C-N) bands and the appearance of new bands in the 580-430 cm⁻¹ region corresponding to ν(M-N) and ν(M-Cl) confirm the formation of metal complexes. It was established …

Emergence Of Norms In The Presence Of Social Network Structure In A Stochastic Evolutionary Game Theory Model, Parker Foresman

Dissertations and Theses

In their 2000 paper, The Emergence of Classes in a Multi-Agent Bargaining Model, Axtell, Epstein, and Young introduced an agent-based bargaining model to study how social norms can emerge and persist from the decentralized, asynchronous interactions of individual agents in the presence of noise, even when these agents have no predefined preferences or advantages. This thesis extends their model by incorporating structured interaction networks, allowing us to investigate how the topology of agent interactions influences long-run dynamics and norm formation. Using a custom simulation package built in the Julia programming language, we replicate the original results under a complete …

Retracted: High-Performance System For Predicting Icu Patient Durations Using Artificial Neural Networks With Transfer Learning, Mahmood. K. Awsaj, Yousif Al Mashhadany, Lamia Chaarifourati

Iraqi Journal for Computer Science and Mathematics

In the wake of disease outbreaks such as COVID-19, real-time health monitoring and prediction systems have become essential for ensuring effective patient care. These systems rely on sensors to monitor biometric parameters such as blood pressure, body temperature, and heart rate, providing continuous and accurate data that medical staff cannot collect manually around the clock. This study presents a robust framework for managing Intensive Care Unit (ICU) patients using Artificial Neural Networks (ANN) with Transfer Learning. The data is analyzed across five distinct time windows, each representing a period of ICU stay based on vital signs and medical test results. …

From Chemistry To Conscious Code: How Ai Is Shaping The Next Generation Of Sustainable Innovation, Shehan S. Makani

STEM for Success Showcase

In a world racing toward an uncertain future, we stand at the thrilling intersection of chemistry, artificial intelligence, and human resilience. For many of us in STEM, this is no longer just about mastering equations or coding algorithms - it’s about engineering the future itself. At NJIT, we are not just students and researchers - we are builders, thinkers, and dreamers. And the truth is, the world needs dreamers with tools now more than ever.

Advancing Single-Molecule Nanopore Sensing: From Protein Conformation And Metal Binding To Dna Transport Via Hybrid Nanostructures, Matthew J. Odonohue

Multidisciplinary Studies Theses and Dissertations

This dissertation presents the development and application of solid-state and hybrid nanopore platforms for single-molecule analysis of biomolecular interactions. First, a silicon nitride nanopore was employed to characterize the conformational states of the transferrin receptor (TfR), enabling discrimination between its monomeric and dimeric forms, as well as from its ligand, human serum transferrin (hSTf), despite their similar molecular weights. In a separate study, the same nanopore configuration was used to track the real-time binding of titanium ions to transferrin, demonstrating single-molecule sensitivity to metal-induced conformational changes. A hybrid microtubule–solid-state nanopore (MT–SSN) was then engineered, repurposing microtubules as electrostatically anchored conduits …

Novel Nanomaterials For Developing Bone Scaffolds And Tissue Regeneration, Nazim Uddin Emon, Lu Zhang, Shelby Dawn Osborne, Mark Allen Lanoue, Yan Huang, Z. Ryan Tian

Cell and Molecular Biology Faculty Publications and Presentations

Nanotechnologies bring a rapid paradigm shift in hard and soft bone tissue regeneration (BTR) through unprecedented control over the nanoscale structures and chemistry of biocompatible materials to regenerate the intricate architecture and functional adaptability of bone. This review focuses on the transformative analyses and prospects of current and next-generation nanomaterials in designing bioactive bone scaffolds, emphasizing hierarchical architecture, mechanical resilience, and regenerative precision. Mainly, this review elucidated the innovative findings, new capabilities, unmet challenges, and possible future opportunities associated with biocompatible inorganic ceramics (e.g., phosphates, metallic oxides) and the United States Food and Drug Administration (USFDA) approved synthetic polymers, including …

Coding For Nonstandard Channels: A Capacity-Oriented Approach, J Daniel Harman

Theses and Dissertations

This work addresses the issue of coding for non-standard channel models with an emphasis on capacity-oriented channel analysis and system design. The first channel studied is the Wyner wiretap channel. Channel sounding results were obtained to calculate the capacity for the multi-antenna case of the Wyner wiretap channel to ascertain the potential for secure communication via secrecy coding. The second channel studied is an asymmetric binary crossover erasure channel (ABCEC) with a short-blocklength constraint. The capacity for this channel was derived, and linear codes were chosen and tested. Another study was conducted on the ABCEC within a degraded wiretap channel …

Volumetric Imaging Of Cellular Dynamics Using Light-Sheet Microscopy: From Subcellular Structures To Whole Organisms, Md Nasful Huda Prince

Optical Science and Engineering ETDs

Advancing biomedical imaging requires platforms that combine high-resolution, speed, and stability across biological scales. This dissertation presents three innovations designed to address limitations of conventional light-sheet and oblique plane imaging. First, we introduce Reflected Inline Detection in Epi-Oblique Plane Microscopy (RIDE-OPM), a compact and drift-resistant system that eliminates a tertiary detection path while maintaining alignment flexibility, enabling robust long-term live imaging. Second, we present a high-speed Axially Swept Light-Sheet Microscopy (ASLM) platform for cleared tissues, using dual foci and synchronized sweeping to achieve isotropic sub-micron resolution at up to 40 frames-per-second, a fourfold improvement over standard ASLM. Integrated with a …

Recent Advances In Developing Cell-Free Protein Synthesis Biosensors For Medical Diagnostics And Environmental Monitoring, Tyler P. Green, Joseph P. Talley, Bradley Charles Bundy

Faculty Publications

Cell-free biosensors harness the selectivity of cellular machinery without living cells’ constraints, offering advantages in environmental monitoring, medical diagnostics, and biotechnological applications. This review examines recent advances in cell-free biosensor development, highlighting their ability to detect diverse analytes including heavy metals, organic pollutants, pathogens, and clinical biomarkers with high sensitivity and specificity. We analyze technological innovations in cell-free protein synthesis optimization, preservation strategies, and field deployment methods that have enhanced sensitivity, and practical applicability. The integration of synthetic biology approaches has enabled complex signal processing, multiplexed detection, and novel sensor designs including riboswitches, split reporter systems, and metabolic sensing modules. …

Joint Kinematics And Kinetics During Vertical Jump Landings With Progressively Increasing External Loads, Darcie L. Yount

Dissertations

This study examined sagittal and frontal plane kinematics and kinetics during countermovement jump (CMJ) landings performed with either a traditional barbell (BB) or a hexagonal bar (HEX) across increasing submaximal loads in recreationally active males. Generally, hip and knee flexion angles and joint mean angular velocities decreased as loads increased. Hip extension and knee flexion moments increased as load increased up to 20 kg, at which point these values decreased. Performing the CMJ with the HEX exhibited increased frontal plane stability compared with the BB group. Additionally, the BB group exhibited stiffer landing strategies, with decreased sagittal plane joint angles …