Digital Commons Network^™

Hybrid Differentiator-Based Method For Estimating The Drilling Speed Of A Vehicle-Mounted Drilling Rig During Surface Drilling For Mine Rescue, Wang Yunfei, Zhao Jiyun, Hu Yongjie, Zhang Zongyang, Pan Xiaochen

Coal Geology & Exploration

Background Vehicle-mounted drilling rigs (VMDRs), key devices in surface drilling for mine rescue, directly connect the ground and underground disaster-hit areas through vertical drilling, thereby creating rescue channels for trapped people. In this case, the drilling speed of VMDRs serves as an important indicator for evaluating surface drilling operations. However, directly determining the drilling speed is challenging due to the limitations of position sensors. Methods This study developed an estimation method based on a new hybrid differentiator (NHD), allowing the drilling speed to be calculated using measurable displacement signals. First, a continuous switching function was devised, and its convergence characteristics …

Methodology And Integration For Dynamic Safety Assessment And Early Warning Of Drilling Sites For Emergency Rescue After Underground Space Collapse, Xie Zhongzhi, Zhang Guohua, Wei Xingtong, Zhang Ning, Wang Xiaotian, Ni Xiaoyang, Zhou Zhengwen, Wang Xianze

Coal Geology & Exploration

Objective IGiven the lack of methods for dynamic safety assessment related to disasters in drilling site environments for emergency rescue after underground space collapse, this study proposed a multisource data-driven methodology for dynamic safety assessment and real-time early warning. Methods By comprehensively considering the disaster-inducing environment and disaster-causing factors at drilling sites for emergency rescue, this study established the safety assessment indices of the environment and their grading system. By integrating multi-source data on surrounding rock deformation, gas concentration, and drilling rig vibration, this study developed a safety assessment method through combination weighting achieved using the analytic hierarchy process (AHP) …

Long-Term Degradation Of Natural-Fiber Coir Turf Reinforcement Mat Installed Along An Urban Stream In South Carolina, Kayden G. Rothery, Bob L. Lowe

Research from the Berry Summer Thesis Institute, 2025

Nature-based (or “living”) solutions to shoreline restoration are attractive due to their positive contributions to ecosystem function and service provision in coastal and estuarine environments. However, non-biodegradable plastics are widely used in their implementation; for instance, plastic-based geotextile fabrics are used to stabilize soil and organic matter to prevent loss of sediment fill. Paradoxically, these polymer-based geotextiles intended to protect the estuarine environment are unintended sources of plastic pollution as they inevitably degrade over time. Consequently, there is significant interest in transitioning from synthetic polymer-based geotextiles to sustainable non-plastic alternatives. To begin addressing this compelling research opportunity, this project aims …

Using Statistical Clustering Of Trajectory Data To Support Analysis Of Subject Movement In A Virtual Environment, Martin Galicia Avila, Douglas Timmer, Alley C. Butler

Manufacturing & Industrial Engineering Faculty Publications

Gracia de Luna conducted experiments with an HMD virtual environment in which human subjects were presented with surprise distractions. His collected data for head, dominant hand, and non-dominant hand included 6 DOF human subject trajectories. This paper examines this data from 57 human subject responses to those surprise virtual environment distractions using statistical trajectory clustering algorithms. The data is organized and processed with a Dynamic Time Warping (DTW) algorithm and then analyzed using the Density Based Spatial Clustering (DBSCAN) algorithm. The K-means method was used to determine the appropriate number of clusters. Chi Squared goodness of fit was used to …

Digital Rock Modeling And Elastic Simulations Of Fractured-Vuggy Carbonate Reservoirs, Zhu Wei, Zhao Luanxiao, Wang Yirong

Coal Geology & Exploration

Objective The Tarim Basin exhibits extensively distributed deep fractured-vuggy carbonate reservoirs. Therefore, determining the seismic and petrophysical characteristics of carbonate reservoirs with a fracture and vug system will provide significant guidance for the exploration, discovery, drilling, and exploitation of favorable reservoirs in the basin. Variations in the filling state of dissolution fractures and vugs will lead to significantly different elastic and attenuation characteristics of the reservoirs. Methods Based on geological insights into the geometric structural and filling characteristics of the dissolution fracture-vug system in the outcrop sections of carbonate strata in the Tarim Basin, this study proposed a digital rock …

Boosted Electrochemical Oxidation Of Methanol At Feox/Pd/Au Ternary Nanocatalyst, Ghada El-Nowihy

Chemical Engineering

In this paper, a highly effective catalyst for methanol electrochemical oxidation (MEO) was developed by the sequential deposition of spherical gold nanoparticles (nano-Au) on a glassy carbon (GC) rod’s surface followed by palladium nanoparticles (nano-Pd), then, iron oxide nanoparticles (nano-FeOx) to form FeOx/Pd/Au/GC catalyst. The sequence of catalyst’s layers proved crucial in optimizing its performance towards MEO. Notably, FeOx/Pd/Au/GC electrode produced a remarkable enhancements in the catalytic activity (by 7 times) and stability (approximately 16 fold) when compared to those produced at Pd/GC rod. Additionally, the onset potential for MEO was significantly reduced by approximately 40 mV. Through material and …

08.25.2025 Ored Connect, Liz Williamson

ORED Newsletter

• Research Security Required Training
• ORED Research Reception

Table Of Contents, The Editors

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 …