Digital Commons Network^™

Experimental Study On Shear Resistance Of Existing Prestressed Concrete Hollow Slabs, Lu Youfu, Di Jin, Qin Fengjiang, Sun Yifei

Journal of China & Foreign Highway

Prestressed concrete hollow slabs are widely used in small- and medium-sized span highway bridges in China.With the increase in load ratings,many hollow slab bridges built in early periods fail to meet the current specifications for shear bearing capacity.Nevertheless,these hollow slabs continue to function adequately under existing vehicle loads,making it essential to conduct an in-depth investigation into their shear bearing capacity.Shear bearing capacity tests were conducted on five 16 m-span hollow slabs dismantled from a renovation and expansion project,including three top slabs with a 15 cm cast-in-place concrete structural layer.Various parameters,such as load-displacement curves,web concrete strain,cracking loads,crack development patterns,failure modes,and ultimate …

Analysis Of Flexural Bearin G Ca Pacity Of Concrete Box Girders Reinforced With, Cfrp Mesh End Anchorshu Guihua

Journal of China & Foreign Highway

Carb on fiber reinforced polymer (CFRP ) composite s have significant advantages in the reinforcement of concrete structures.It is worth systematically studying the effect and mechanism of applying this material in embedded end-anchored reinforcement of concrete box girders on improving the load-bearing capacity of the component.Based on the Abaqus finite element analysis (FEA ) platform,two numerical analysis models considering slip contact and full bonding contact between CFRP mesh and polymer mortar were established.A full-scale concrete box girder model test was conducted for validation.The results show that the FEA results are in good agreement with the experimental results.The FEA model that …

Study On Mechanical Effect Of Backfill Construction In Orthogonal Underpass Section Of An Open-Cut Interchange Tunnel, Li Qingda, Li Dexing, Yu Tingshun, Han Xi, Liu Jiansong

Journal of China & Foreign Highway

This study investigated the mechanical effects of backfill construction in the orthogonal underpass section of a full-section complex open-cut interchange tunnel.The research was based on the small-clear-distance interchange tunnel project at the extension section of Jinzhou Avenue and Xingguang Avenue in Chongqing.A combined approach of numerical simulation and on-site in-situ test measurement was adopted to analyze the influence of different backfill materials,tunnel cross-section types,and complex construction procedures on the mechanical behavior of the underpass tunnel.The mechanical effects of backfill construction in the orthogonal underpass section were determined.The following findings are revealed:During the backfill construction process,the displacement and internal force of …

Research On Automatic Review Method Of Highway Bim Based On Knowledge Graph, Wang Wuyu, Shao Kebo, Zhang Feng

Journal of China & Foreign Highway

Building information modeling (BIM ),characterized by multi-dimensional modeling and multi-source data integration,is becoming a key technology to promote the innovation and digital transformation of the highway design industry.Taking BIM as the design deliverable is an inevitable trend in the future development of highway engineering design.However,the current highway BIM review is mainly conducted manually,which brings about problems such as low efficiency,susceptibility to errors,and high subjectivity.This makes it difficult to adapt to the review requirements of the three-dimensional (3D) digital design mode.To this end,this study proposed an automatic review method of highway BIM based on a knowledge graph.By constructing the knowledge graph …

Research On Prefabrication Scheme Of Concrete Barrier Based On Anchorage In Cast-In-Situ Layer, Liang Xiaochong, Kang Hanjing, Luo Yongchuan, Wen Zhiquan, Deng Bao

Journal of China & Foreign Highway

In order to improve the on-site construction efficiency of bridge concrete barriers,a prefabrication scheme for bridge concrete barrier installation was proposed.A structure scheme was developed by anchoring transverse steel bars at the bottom of prefabricated wall panels into the integrated cast-in-situ layer,adopting tongue-and-groove connections between adjacent wall panels,and optimizing the structural configuration and reinforcement of the panels.According to the requirements of the Level VI protection standard,full-scale impact tests using a large bus and a large truck were organized in turn.The results show that the prefabricated concrete bridge barrier can effectively withstand continuous impacts from both large buses and trucks.Its structural …

Influence Of Horizontal Curve Radius On Vehicle Fuel Consumption And Pollutant Emissions On Tw O-Lane Highways, Tu Shengwen, Lei Meimei, Guo Wenyu

Journal of China & Foreign Highway

To investigate the influence of horizontal curve radius on vehicle fuel consumption and pollutant emissions on two-lane highways,this study developed a model based on running speed theory,vehicle driving dynamics,and a fuel consumption and emission analysis system.Fuel consumption and emissions of CO2,CO,HC,and NOx were calculated for a passenger car traveling along horizontal curves under three design speeds:40 km/h,60 km/h,and 80 km/h.The results show that the horizontal curve radius has a significant effect on vehicle fuel consumption and pollutant emissions.The trends of fuel consumption,CO2,and NOx emissions vary consistently with curve radius,while CO and HC emissions follow a similar but different trend.At the …

Reference And Inspi Ration From Low-Carbon Development Experience Of Foreign Highway Infrastructure, Lu Chunying, Gao Shuohan, Du Xueyua N, Jian Li, Wang Xinjun, Kong Yaping

Journal of China & Foreign Highway

In order to provide supp ort for the scie ntific formulation of low-carbon highway infrastructure development policies in China,empirical analysis,comparative analysis,and other methods were utilized,and the construction goals and action strategies of low-carbon highway infrastructure in the United Kingdom,the United States,and Australia were sorted out.Advanced experience in the development of low-carbon highways abroad was summarized.On this basis,combined with the current situation and existing problems of low-carbon development of China ’s highway infrastructure,the following countermeasures and suggestions were proposed:① strengthening top-level design and formulating low-carbon development goals and paths;② implementing the concept of life cycle and strengthening the research and promotion …

Exploring Different Metal-Oxide Cathode Materials For Structural Lithium-Ion Batteries Using Dip-Coating, David Petrushenko, Thomas Burns, Paul Ziehl, Ralph E. White, Paul T. Coman

Faculty Publications

In this study, a selection of active materials were coated onto commercially available intermediate modulus carbon fibers to form and analyze the performance of novel composite cathodes for structural power composites. Various slurries containing polyvinylidene fluoride (PVDF), active material powders, 1-methyl-2-pyrrolidone (NMP) and carbon black (CB) were used to coat carbon fiber tows by immersion. Four active materials—lithium cobalt oxide (LCO), lithium iron phosphate (LFP), lithium nickel manganese cobalt oxide (NMC), and lithium nickel cobalt aluminum oxide (NCA)—were individually tested to assess their electrochemical reversibility. The cells were prepared with a polymer separator and liquid electrolytes and assembled in 2025-coin …

Learning Structure With Multivariate Information Bottleneck And Exploration Of New Methods In Sequential Decision Making, Volodymyr Makarenko

Master's Theses

Research in useful information extraction has been motivated by the increasing demand to extract insights from unstructured data, and by the need to store and transmit great volumes of information, often originating in unstructured data such as videos. Research in rate distortion and information bottleneck paved the path for understanding and guiding the design of lossy encoders, capable of extracting relevant information. Independently, research in deep representation learning has enabled numerous applications for unstructured high-dimensional data such as images. However, the interpretability of the deep learning methods remained limited. Several desired properties of learned representations have been suggested, including disentanglement. …

Autonomous Uav Swarm Formation Utilizing Gradient-Driven Contour Mapping For Radiation Source Localization, Edgar Amalyan

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis presents a drone swarm for radiation mapping to aid source localization. The Department of Energy advocates employing UAVs for this task, but existing approaches remain inefficient and impractical in real-world scenarios. Three custom drones are built and flight-tested. A control algorithm to follow a contour, a constant-intensity path, is designed using a gradient fit. By knowing the source’s direction, the drone swarm can fly in the optimal trajectory at every step, leaving nothing to assumption. A program is created that implements formation flight and autonomous navigation. It is tested via a software-in-the-loop simulation utilizing radiation sources and detectors …

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 …

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, …

Beyond Single Metrics: A Holistic Benchmarking Framework For Low-Power Embedded Systems, Hassan Adam

UNLV Theses, Dissertations, Professional Papers, and Capstones

Modern embedded systems encounter a notable challenge in evaluation. While devices may meet traditional benchmarks, they often underperform in real-world applications due to neglected interactions at the system level. Current benchmarking suites, such as MLPerf Tiny and EEMBC ULPMark, evaluate specific metrics including computational throughput, energy efficiency, and memory usage. However, they do not consider the complex interdependencies that affect real-world performance. This thesis presents a benchmarking framework that concurrently evaluates multiple performance dimensions under realistic workloads, revealing system behaviors that are often hidden in conventional benchmarks.Through the comprehensive evaluation of three representative algorithms: Fast Fourier Transform, quantized neural network …

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: …

Integrated Uav Platform For Multi-Spectral, Thermal, And Eos Imaging In Wildfire Monitoring And Modeling, Md Shariful Islam

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis presents the design and development of a modular unmanned aerial vehicle (UAV) system based on a quadcopter platform for flexible and efficient wildfire-related multimodal image acquisition. Addressing key limitations in ecological UAV monitoring such as sensor inflexibility, time-consuming reconfiguration, and imprecise image georeferencing, the system introduces a versatile payload integration framework supporting three distinct imaging sensors: MicaSense Altum-PT, FLIR Vue Pro R, and Sony Alpha 6000.All onboard components, including the flight controller, autopilot software, GNSS module, motors and ESCs, were selected to optimize stability and payload performance. A gimbal-free, downward-facing mount simplifies field deployment, while custom integration enables …

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 …

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 …

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 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 …

Safe Real-Time Obstacle Detection And Navigation Using Cbf–Clf And Cbf–Pid Control, Nicolas M. Hernandez

McKelvey School of Engineering Graduate Student Theses & Dissertations

Traditional robotic navigation pipelines typically follow a three stage architecture: obstacle detection, path planning, and low-level control for trajectory tracking. While effective in static environments, these methods often introduce latency and lack formal guarantees of safety in dynamic or unplanned for scenarios. Our work addresses these limitations by developing a real-time controller grounded in Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs), unified through a Quadratic Program (QP). We first investigate a hybrid CBF-PID-QP controller on a 1/10 scale car, where the CBF serves as a real-time safety filter, modifying the PID output to prevent constraint violations. While this …

Csa-Xai: Channel–Spatial Attention And Explainable Ai In A Modular Multi-Backbone Framework For Lung Cancer Classification, Omar Ibrahim Obaid, Abdulbasit Alazzawi

Iraqi Journal for Computer Science and Mathematics

Computed tomography (CT) scans require precise and early lung cancer detection to produce better clinical results. High accuracy in deep learning approaches (DL) poses an existing challenge to interpret their functionality effectively. This research presents an innovative modular multi-backbone structure that combines channel-spatial attention together with explainable AI (XAI) methods for three-class lung cancer diagnosis (Normal, Benign, and Malignant). Research was carried out to evaluate six pre-trained CNN backbones (ResNet-50, VGG19, Inception-V3, EfficientNet-B0, MobileNet-V2, DenseNet-121) which received hybrid attention enhancement on the IQ-OTH/NCCD dataset. The experimental data showed four pre-trained models reaching perfect accuracy at 100 percent whereas the others …

Orbital Maneuvers And Interplanetary Trajectory Design Via Reinforcement Learning, Roberto Cuéllar Rangel

Doctoral Dissertations and Master's Theses

This dissertation investigates the application of reinforcement learning (RL) to the design and optimization of low-thrust spacecraft trajectories, with an emphasis on autonomy, adaptability, and robustness in the presence of system uncertainties and unmodeled perturbations. Classical approaches to low-thrust trajectory design are predominantly grounded in optimal control theory, which relies on the availability of precise dynamical models and often requires problem-specific reformulation and solver tuning. While optimal control methods offer high accuracy under deterministic conditions, their sensitivity to stochastic disturbances and computational limitations in highly nonlinear or uncertain environments pose significant challenges for future autonomous space missions.

To address these …

Investigating Radiation Induced Rowhammer And Retention Failures In Dram, Tyler Ricks

Theses and Dissertations

It is desirable for DRAM devices to be used in harsh radiation environments. Exposure to radiation causes DRAM cells to lose their retention time as their ability to store charge effectively declines. Exposure also worsens the effect of disturbance that causes neighboring cells to flip when a row is opened multiple times: an effect known as the Row Hammer Effect. This thesis evaluates the impact of radiation-induced memory cell degradation by comparing memory cell's retention time and their resilience to the row hammer vulnerability. Three generations of DRAM were placed under a neutron radiation beam. Sections of the irradiated devices …

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 …

Quantified Possibility Neutrosophic Soft Set Based Decision Support System For Enhanced Accuracy In Sustainable Supplier Selection Decision Analysis, Neha Andaleeb Khalid, Muhammad Saeed

Neutrosophic Systems with Applications

To deal with the concepts of vulnerability, ambiguity, and indeterminacy that are typical in complex decision analysis contexts, neutrosophic set-like structures are frequently used. Although neutrosophic sets are particularly good at handling indeterminate circumstances, indeterminacy plays a part in making the decision making process imprecise and ambiguous. This paper develops a more advanced technique to improve the accuracy of decision analysis problems: the Quantified Possibility Neutrosophic Soft Set Decision Support System (Q^t PNSSDSS). In order to reduce the element of indeterminacy present in conventional neutrosophic sets, the proposed DSS is based on the Quantified Neutrosophic Set, which employs a …

Some Properties Of Neutrosophic Cubic Hypersoft Sets, Lubna Nayab, Muhammad Gulistan, Fawad Hussain

Neutrosophic Systems with Applications

This study addresses the limitations of NCS in managing uncertainties associated with multiple attributes and their further bifurcation. To address this challenge, we propose a generalization of the neutrosophic cubic soft set, introducing the concept of "neutrosophic cubic hyper soft set." Within this framework, we define internal and external neutrosophic cubic hypersoft sets, along with operations such as P-intersection, P-union, P-restricted union, P-extended intersection, P-OR operator, P-AND operator, R-intersection, R-union, R-restricted union, R-extended intersection, R-OR operator, R-AND operator, complement, and relative complement of neutrosophic cubic hyper soft sets. The study explores and presents relevant results, demonstrating the enhanced capability of …

Application Of Multi-Criteria Decision Analysis For Quantifying Responsibility In Ml-Based Intrusion Detection System, Mona Mohamed, Zekra Sakr

Neutrosophic Systems with Applications

With the swift growth in Internet of Things (IoT), certifying secure and trustworthy networks has turned to be a critical challenge, particularly as IoT devices are increasingly vulnerable to sophisticated cyberattacks. As a remedy, intelligent intrusion detection systems (IDS) evolved as promising solutions in recent years, but deciding on the appropriate model remains difficult because of competing performance and trustworthiness criteria. To this end, this paper explores a novel application of an ML-augmented decision-making framework to enhance security-related decision-making in IoT environments. The framework systematically evaluates and ranks ML-based IDS systems according to different evaluation criteria with distinct trade-offs, including …

Compact Heterogeneous Architectures And Algorithmic Methods For Enhanced Real-Time Digital True Time Delay Beamforming And Radar Applications, Nathan Lynn Burnett

Theses and Dissertations

Modern radar and signal processing systems require efficient computational architectures to handle increasing data rates while still being able to maintain dynamic configurability and complex tasks. This thesis presents and explores hardware and algorithmic approaches addressing these demands across several different applications. First, a low-cost tracking platform and architecture for unmanned aerial vehicles (UAVs) is developed, which provides detection and tracking capabilities, compliant with new FAA remote ID regulations. Second, a GPU-FPGA based digital true time delay beamformer is implemented to achieve real-time wideband radio frequency interference (RFI) mitigation. Specifically this beamformer explores weight calculation and calibration through particle swarm …