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Coal Geology & Exploration

No abstract provided.

Fracturing Evolution Mechanisms Of Eroded Coals Under The Temperature Effect Of The Co2-H2O System, Lin Baiquan, Shi Yu, Liu Ting, Shen Yang, Huang Tao

Coal Geology & Exploration

Background Injecting hot flue gas produced and discharged by gas-fired power plants into deep coal seams where gas is difficult to extract enjoys dual benefits: gas production growth and geologic CO₂ sequestration. However, there is an urgent need to determine the impact of the heat-carrying property of hot flue gas on the stability of coal reservoirs. To address this issue, the key is to clarify the fracturing evolution mechanisms of eroded coals under the temperature effect of the CO₂-H₂O system.Methods Using the independently built platform of CO₂-H₂O-coal interactions that consider …

Advances In Research On Geological Evaluation Of Compressed Air Energy Storage In Underground Gas Storage Facilities, Jiang Wen, Huang Leqing, Zhang Songhang, Ma Huiying

Coal Geology & Exploration

Background Against the backdrop of achieving peak carbon dioxide emissions and carbon neutrality globally, renewable energy has developed rapidly and is gradually changing the world's energy mix. However, the intermittent and fluctuating nature of photovoltaic and wind power energy is prone to cause grid frequency fluctuations and reduced power supply reliability. Compressed air energy storage (CAES) technology has emerged as the key to the stable operation of the green power grid thanks to its large capacity and low cost.Methods Based on a systematic review of domestic and international literature in recent years, this study organizes the current theoretical and …

Macroscopic Failure Characteristics And Seepage Pattern Of Damaged Goaf-Side Coal Pillars Under Confined Water Accumulation, Liu Weitao, Chen Dongqi, Liu Yuben, Du Yanhui, Zhao Jiyuan

Coal Geology & Exploration

Background The method of supporting roadways using small coal pillars has been widely applied in China. As a result, the stability of goaf-side coal pillars affects mining safety. Resulting from multiple disturbance effects, coal pillars tend to contain various types of damage fractures. The confined water environment in goaves with water accumulation might cause the instability failure of damaged goaf-side coal pillars. Methods To investigate the impacts of the damage fracture structure on coal stability, this study introduced the concept of relatively stable initial damage fractures. Based on the theory of damage mechanics, this study constructed hydro-mechanical-damage (HMD) coupling models …

Pseudo-2d Mt Inversion Technique For Small-Frame Tem And Its Application In Advance Geological Prediction, Han Ziqiang, Wanma Longzhi, Li Xiangfeng, Yang Yongqiang, Zhu Xiaoming, Luo Chengxian, Tang Li

Coal Geology & Exploration

Objective To address the bottlenecks of low computational efficiency and impracticality of multi-dimensional inversion in the advance geological prediction based on the transient electromagnetic method (TEM), this study developed a pseudo-two-dimensional (2D) magnetotelluric (MT) inversion technique for small-frame TEM based on time-frequency transformation. Methods First, this study corroborated the significant similarity between the late-time apparent resistivity derived using small-frame TEM and the Cagniard apparent resistivity in the MT method under the parameters commonly used in advance geological prediction. Then, the optimal time-frequency transformation coefficient (C_TF) was introduced to achieve a rapid conversion from the late-time apparent resistivity …

Probing The Effect Of Peg-Dna Interactions And Buffer Viscosity On Tethered Dna In Shear Flow, Fatema Tuz Zohra, Huda Al-Zuhairi, Jefferson Reinoza, Hyeongjun Kim, Andreas Hanke

Mechanical Engineering Faculty Publications

DNA flow-stretching is a widely employed, powerful technique for investigating the mechanisms of DNA-binding proteins involved in compacting and organizing chromosomal DNA. We combine single-molecule DNA flow-stretching experiments with Brownian dynamics simulations to study the effect of the crowding agent polyethylene glycol (PEG) in these experiments. PEG interacts with DNA by an excluded volume effect, resulting in compaction of single, free DNA molecules in PEG solutions. In addition, PEG increases the viscosity of the buffer solution. By stretching surface-tethered bacteriophage lambda DNA in a flow cell and tracking the positions of a quantum dot labeled at the free DNA end …

3d-Printable Hydrogel With Rapid Ambient Self-Healing, Anna M. Petre

Research from the Berry Summer Thesis Institute, 2025

Inspired by nature, soft robots composed of compliant (“soft”) materials are well-suited for uncertain, dynamic tasks requiring safe interaction between a robot and its environment. Soft robots with the ability mend minor damage (e.g. perforations, tears) have been enabled by the rapid development of self-healing soft materials. Recently, one of these hydrogel materials (internally dubbed “BeckOHflex”) – made entirely from commercially available precursors – was developed with several appealing characteristics including rapid ambient self-healing, 3D printability on commercial machines, and competitive mechanical properties. However, the self-healing performance of BeckOHflex falls short of competitors that leverage custom-synthesized precursors. This project aims …

Intelligent Motion Tracking: A Low-Cost Surveillance Framework Using Soc Devices And Sensor Fusion, Aung Myat Khaung, Nicholas Michael Stiffler

Research from the Berry Summer Thesis Institute, 2025

This thesis presents the design and implementation of a lightweight surveillance system capable of realtime motion detection, object tracking, and behavioral history reconstruction in controlled environments. The system uses System-on-Chip devices such as Raspberry Pi boards equipped with NOIR cameras, monocular cameras, and break-beam sensors that work together to detect and track single or multiple moving objects like colored balls. The prototype is validated in structured settings with the goal of eventual deployment in more dynamic environments, addressing the challenge of reliably tracking visually similar objects with minimal distinguishing features. The architecture integrates computer vision with sensor fusion by combining …

An Investigation Of Hydro-Morphological Changes At The Estuarine Outlet Of Kitchener Drain, Northern Coast Of Egypt, Nada Mansour, Karim Nassar, Mahmoud El-Gamal, Tharwat Sarhan

Mansoura Engineering Journal

In recent years, sedimentation and coastline erosion have occurred in the Kitchener Drain, where its outflow meets the Mediterranean Sea. Several measures have been taken to mitigate the impact of these problems. Excavation continues toward the drain's outlet, and there have been multiple stages of hard protection work, such as groins along the drain's eastern and western banks. What would happen if the working near the drain was kept, where accretion and erosion are most severe. The Coastal Modelling System (CMS) was used to find it out. The two-dimensional hydrodynamic circulation model known as the CMS is outfitted with modules …

Novel Nano-Composite Materials (Nncms) With The Designations J1, J2, And J3 For Removal Several Harmful Ions, Mohammed K. Al-Doseri, Safaa. R. Fouda, Mohamed Mossad, Mahmoud H. Mahmoud

Mansoura Engineering Journal

The most toxic material to the environment is heavy metals. remove heavy metals from wastewater is done by adsorption. Many papers have been done using different materials to take off toxic dangerous ions from wastewater. Noval Nano-composites material (NNCM)as designated as J1, J2, and J3 the most used adsorbents in this study. To conduct the experiments, synthetic wastewater was generated in the lab. Batch experiments were conducted to create the best conditions for removing these ions from the wastewater. The optimum conditions obtained were 90 min optimum contact time, and pH equal seven, optimal NNCM dose was 0.5 g/L achieving …

Learning Neural Point Processes For Long Event Sequences, Zhuoqun Li

LSU Doctoral Dissertations

This research presents a comprehensive series of studies aimed at advancing learning neural point processes for long event sequences, with applications spanning disaster resilience, crime forecasting, and healthcare. Structured around three interconnected studies, this work addresses core challenges in temporal point process (TPP) modeling, efficient handling of long event sequences, and improving accuracy over extended forecasting horizons by reinforcement learning.

The first study proposes the Sparse Transformer Hawkes Process (STHP) to model long asynchronous event sequences. Traditional neural network-based TPPs struggle with long event sequences due to computational inefficiencies. To address this, the STHP model combines two components: a temporal …

Transfer Learning-Based Multi-Sensor Approach For Predicting Keyhole Depth In Laser Welding Of 780dp Steel, Byeong-Jin Kim, Young-Min Kim, Cheolhee Kim

Mechanical and Materials Engineering Faculty Publications and Presentations

Penetration depth is a critical factor determining joint strength in butt welding; however, it is difficult to monitor in keyhole-mode laser welding due to the dynamic nature of the keyhole. Recently, optical coherence tomography (OCT) has been introduced for real-time keyhole depth measurement, though accurate results require meticulous calibration. In this study, deep learning-based models were developed to estimate penetration depth in laser welding of 780 dual-phase (DP) steel. The models utilized coaxial weld pool images and spectrometer signals as inputs, with OCT signals serving as the output reference. Both uni-sensor models (based on coaxial pool images) and multi-sensor models …

Exploring Arabic Large Language Models: A Comprehensive Review, Lamar Aljahdali, Joud Kaki

Effat Undergraduate Research Journal

This paper presents a comprehensive review of Arabic large language models (LLMs), exploring their capabilities, limitations, and potential impact on the Arabic NLP landscape. We analyze the performance of prominent LLMs, including JAIS, AraBERT, and BLOOM, highlighting their strengths and weaknesses on various NLP tasks. The review delves into critical challenges faced by Arabic LLMs, such as domain adaptation, cross-lingual capabilities, and ethical considerations. Additionally, the paper emphasizes the importance of responsible development and deployment practices for LLMs, ensuring fairness, transparency, and cultural sensitivity.

Analog To Digital Converters Topologies For Radar System Application: A Comparison, Kulsoom Mateen, Wafa Alharbi, Aziza I. Hussein

Effat Undergraduate Research Journal

Analog-to-digital converters (ADCs) that convert analog signals into digital ones play a significant role in radar systems. The accuracy and resolution of radar readings are significantly influenced by the quality and performance of ADCs. This paper discusses and compares the application of five different types of ADCs in radar systems. It also elaborates on each ADC's working principle, advantages, and limitations. The parameters compared are resolution and dynamic range, signal-to-noise ratio (SNR), latency and sampling rate, power consumption, size, and cost. After thorough research, we concluded that each ADC differs depending on the designer’s desired application. For example, flash ADCs …

Satellite Internet Technology: Connect The Unconnected, Nora Turki Alamoudi Ms., Aziza I.Hussien Proffessor

Effat Undergraduate Research Journal

Satellite Internet is the internet provided through a network of communication satellite constellations in space. This type of internet can cover an area of a wider range than the commonly used cable internet. The development of small-sized satellite technology in the commercial space sector has entered a vigorous phase of development. However, approximately four billion individuals worldwide lack internet access. Most of these citizens live in developing countries. It is in this context that the industry of satellite internet is expanding its social and economic development through connectivity. The implementation of the idea requires examining the advantages provided by satellite …

Sar Analog-To-Digital Converters For Point-Of-Care Biosensors: A Comparison, Eithar Alammari, Joud Alamro, Sara Alashwali, Ghadah S. Alyami, Aziza I. Hussein

Effat Undergraduate Research Journal

The SAR ADC, recognized for its low power consumption, moderate resolution, and satisfactory processing speed, stands as an ideal choice for ultra-low power biomedical applications. Recent efforts have been concentrated on enhancing the power efficiency of the SAR ADC sub-components through intricate refinements and innovative techniques. These efforts aim to minimize energy consumption without compromising the ADC's performance. Therefore, this paper aims to thoroughly examine various implementation approaches for the main components of the SAR ADC, highlighting their individual strengths, weaknesses, and limitations within wireless biomedical applications.

Electrical Energy Consumption Forecasting Based On Conventional And Artificial Intelligence Methods: A Comparison, Haya H. Binsalim, Jana Kamal, Salma Badaam, Danah Milyani, Ghadah S. Alyami, Aziza I. Hussein

Effat Undergraduate Research Journal

Artificial Intelligence (AI)-based models have been widely applied for energy consumption forecasting over the past decades. The purpose of this paper is to review and compare the classical techniques and the emerging new AI-based techniques used for forecasting electrical energy consumption in buildings. The findings revealed that the Artificial Neural Network (ANN) model achieved the lowest Mean Absolute Percentage Error (MAPE) of 0.928%. AI-based techniques have many advantages over classical techniques, such as their ability to handle a large amount of data and provide accurate and fast results.

Intelligent System Designs For Hvac Energy Reduction In Buildings: Ai-Based Forecasting And Hybrid Active/Passive Approaches, Leena N. Alam, Rim M. Obaid, Thoraya Musa, Wegdan O. Alshateri, Passent M. Elkafrawy Prof

Effat Undergraduate Research Journal

The majority of building energy utilization worldwide is related to HVAC (Heating, Ventilation, and Air-Conditioning) systems. Eighty percent of the energy produced in Saudi Arabia is used by buildings, and since 70\% of that energy is used for ventilation, air conditioning accounts for roughly 50\% of the nation’s electrical use. This study reviewed and compared much research that used various AI-based forecasting algorithms. Specifically, the study explored the potential of passive and active cooling methods and intelligent system designs and used this analysis to develop a hybrid model that combined AI-based forecasting with active/passive approaches for optimal energy savings. The …

Continuous Versus Discrete-Time Sigma-Delta Analog To Digital Converters For Biomedical Applications: A Comparison, Haya H. Binsalim, Batool Alaidroos, Basma Shigdar, Salma Badaam, Aziza I. Hussein

Effat Undergraduate Research Journal

Continuous-time (CT) and discrete-time (DT) sigma-delta (ΔΣ) converters are two commonly used techniques for analog-to-digital conversion. While both methods operate based on the principles of oversampling and noise shaping, they differ in their implementation and performance characteristics. CT ΔΣ converters use analog circuits to sample and process signals continuously, while DT ΔΣ utilizes digital circuits to sample and process signals at discrete intervals. This paper presents a comprehensive comparison between CT and DT ΔΣ converters, highlighting their advantages and limitations. The comparison is made in terms of design complexity, power consumption, signal-to-noise ratio (SNR), and other essential parameters in medical …

Wideband Spectrum Sensing For Cognitive Radio Using Under-Sampled Successive Approximation Adc, Aziza I. Hussein

Effat Undergraduate Research Journal

The radio spectrum, an inherently limited resource, has been increasingly utilized owing to the recent exponential growth of wireless services. This has led to a new approach, termed cognitive radio, predicated upon exploitation of spectrum holes for omnipresent spectrum utilization. This is made possible via cognitive radio networks’ employment of spectrum sensing. Wideband spectrum sensing has been the focal challenge point in cognitive radio technology, since existing techniques are reliant on analog to digital converters (ADC) with sampling at the Nyquist rate. Unfortunately, in order to perform digitization of wideband RF signals at the Nyquist rate, a very high sampling …

Fault Detection In Analog Vlsi Circuits Based On Artificial Intelligence

Effat Undergraduate Research Journal

In thepastdecade,artificialintelligence(AI)hasbeenonthe rise asatooltobeutilizedacrossallthefieldsavailableintheindustry. Specificallyinthemicroelectronicsfield,exploringthecurrentoptionsof VLSI faultdetectionandtheirtypesiscrucialforadvancementandis importantinidentifyingwhetherthereisroomforimprovementorthe optimumisalreadybeingdone.Therefore,thepurposeofthisresearch is tocompareandcontrastbetweenVLSIfaultdetectionindigitaland analog circuitsusingAI.Techniquestoimproveefficiencysuchastrou- bleshootinginsmallsegmentsratherthanthewholesystemandsome resolutions todrawbacksthataren’tdetectibletohumansliketimingare discussed andresolvedinthispaper.Moreover,somenon-idealitiesand risks likemarginalstabilitywereconsidered.Finally,presentelements that areusedintoday’sfaultdetectioncircuitslikeneuralcontrollers and theANNswerediscussedaswell.Currently,theANNsarethemost utilized toolforfaultdiagnosesanddetection;however,forthecontinu- ation ofthistechnology’sgrowth,developersneedtofindmoreefficient methodstomovepastit.

Intelligent Energy Management Mechanisms For Electric Vehicles: A Review, Aziza I. Hussein

Effat Undergraduate Research Journal

This paper investigates intelligent methods used for energy management (EM) in Electric Vehicles (EV). The key role of EM in EVs to increase the performance of the vehicle and reduce Fuel Consumption (FC) thus producing less greenhouse effect. However, the used tactics had limitations. An introduced model of Intelligent Energy Management System (IEMS) for Plug-in hybrid electric vehicles (PHEVs) was efficient for FC reduction. Whereas for IEMS Based on Kalman Filtering showed a low percentage of error. While EM using Tags Threshold Admission And Greedy Scheduling improves EV’s performance, but it can only manage energy per one EV. Another model …

Electrical Energy Consumption Forecasting Analysis Based On Conventional And Artificial Intelligence Methods: A Comparison, Aziza I. Hussein

Effat Undergraduate Research Journal

Artificial intelligence (AI)-based models have been widely applied for energy consumption forecasting over the past decades. The purpose of this paper is to review the classical techniques and the emerging new techniques based on AI of the building electrical energy consumption forecasting. The advantages of AI-based techniques over the classical are that AI methods can handle a large amount of data yet gives accurate results, the results can be found very quickly, in addition to AI having the ability to solve complex nonlinear patterns of raw data. This paper will discuss several studies using different models of forecasting based on …

Posture - A Framework With Measures And Mediating Effects In Support Of A Structural Model For Attitude In Identity Formation, Christopher Kyle Prather

Doctoral Dissertations

This dissertation develops a framework and new, valid, abbreviated instruments for researching engineering identity formation as an evolving relational and attitudinal development process rather than a fixed outcome. The framework centers on “posture”, a mediating effect analogous to physical posture in human factors engineering. The investigation was unique, and developed evidence for a structural model using an interdisciplinary, systems-oriented approach. Methodologically, a clinical measure of internal emotional states from the Marriage and Family Therapy literature was administered alongside existing scales for perseverance, perceived workload, and psychological ownership. This study used the broad coverage of the clinical instrument to search for …

Evaluation Of The Performance Of The Traveling Wave Differential Element In Protective Relays, Niranjan Kc

Master's Theses

This thesis evaluates the dependability, security, and limitations of the traveling wave differential protection function (TW87) in modern time-domain-based protective relays, using a combination of simulations and hardware testing in a laboratory environment. Fault transients are first generated using the electromagnetic transients program model of a real, 230 kV, 65.7 km long overhead transmission line, which are then played back on real time-domain-based protective relays. Various fault scenarios are chosen to evaluate the impacts of factors such as fault inception angle, distance to fault from line terminals, fault type, fault impedance, and external faults on the relay functions’ performance. Results …

The Design Of Coplanar Waveguide Traveling-Wave Kinetic-Impedance Parametric Amplifiers, Jordan Scott Savoie

Master's Theses

Astronomical observations and many physics experiments rely on cryogenic amplifiers for readout. Current sensitivity is limited by the noise figure of high-electronmobility transistor (HEMT) amplifiers, which have proven di!cult to decrease further in recent years. Traveling-wave kinetic-impedance parametric amplifiers (TKIPAs) are an emerging class of amplifiers which have the potential to substantially improve the sensitivity of microwave low-noise amplifiers (LNAs) while also accepting relatively high input powers and amplifying over a wide bandwidth. In this thesis, I present the design, modeling, and testing procedures for coplanar waveguide (CPW) TKIPAs developed by our group at the National Radio Astronomy Observatory. Using …

Advancing Rfid Systems: From Head Orientation To Robotic Localization Using Passive Tags, Guilherme Ricardo Mendes Da Silva Barreto De Figueiredo

Doctoral Dissertations

No abstract provided.

An Envisioned And Efficient Design Of Next Generation Decentralized Iot Bot Detection Model, Ahmed Abdullah Almalki

Doctoral Dissertations

The Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) are revolutionizing critical infrastructures, but their expansion has also introduced severe cybersecurity vulnerabilities. Traditional IoT Bot Detection Systems (IBDS) struggle to scale in environments characterized by high-dimensional, large-scale, and redundant network traffic. These challenges hinder the development of reliable cloud-based intrusion detection systems. The limitations of static and rulebased methods in detecting evolving IoT botnet attacks—such as those launched by Mirai and Gafgyt—underscore the need for intelligent, adaptive approaches. To address this, the present study proposes a machine learning and deep learning-driven IoT Botnet Detection Model, validated through …

Surgeon Ergonomics While Performing Total Knee Arthroplasty, Cameron Dahman

Electronic Theses and Dissertations

One of the most prevalent hazards to orthopedic surgeons are musculoskeletal disorders with 97% of arthroplasty surgeons reporting procedural-related musculoskeletal pain and 66% of orthopedic surgeons reporting procedural-related musculoskeletal injury [1], [2], [3]. With the annual number of total knee arthroplasties (TKA) on the rise, orthopedic surgeons are taking on larger annual caseloads and retiring later in life [4], [5]. The goal of this study was to quantify postural demand for 11 discrete surgical steps of TKA to identify ergonomically unfavorable positions surgeons experience while operating. Four orthopedic surgeons performed TKA while their motions were recorded using marker-based motion capture. …

Developing Deep Learning Methods For Cognitive Impairment Detection Based On Non-Invasive Data, Muath Alsuhaibani

Electronic Theses and Dissertations

Cognitive impairment detection is on the rise to help reduce the burden of healthcare costs on institutions and individuals. Mild Cognitive Impairment (MCI) is an early stage of cognitive decline progressing to Alzheimer’s disease (AD) or AD-related Dementia (ADRD). Detecting the early stages of AD/ADRD is crucial for early interventions among older adults to mitigate cognitive decline over time. However, the current diagnostic methods are often costly and/or invasive, such as MRI and PET scans. Thus, the search for non-invasive and cost-effective screening tools for the early detection of cognitive impairment using speech, language, visual, and motor data is growing. …