Digital Commons Network^™

A Seismic Random Noise Suppression Method Based On Cnn-Mamba, Wei Xiujuan, Liu Xingye, Zhou Huailai

Coal Geology & Exploration

Background Seismic random noise suppression is recognized as a key step to improve the quality of seismic data. Data-driven deep learning provides an intelligent solution for the noise suppression. However, mainstream random noise intelligent methods based on convolutional neural networks (CNNs) are constrained by their local receptive fields. This limitation results in insufficient collaborative optimization between local details and macroscopic structures during denoising, further reducing the noise suppression accuracy. Transformer models, which are widely applied to global feature extraction, can effectively capture long-distance dependencies through the self-attention mechanism, theoretically overcoming the limitations of CNNs in global modeling. However, these models …

Levels Of The Nicotine Analog 6-Methyl Nicotine As A Naturally Formed Tobacco Alkaloid In Tobacco And Tobacco Products, James F. Pankow, Wentai Luo, Kevin J. Mcwhirter, Mohana Sengupta, Robert M. Strongin

Civil and Environmental Engineering Faculty Publications and Presentations

S-6-methyl nicotine (S-6MN) has appeared as a nicotine substitute in commercial electronic e-cigarette products and pouches, including with the claim that such use is not regulated under current U.S. law. This work describes an analytical chemistry based search for the natural S/R presence of 6MN and three other MN compounds in additive-free cured leaf tobaccos and in multiple commercial tobacco products. The samples were extracted using 5 N NaOH, then methyl t-butyl ether. The extracts were analyzed using gas chromatography (GC) with mass spectrometric (MS) detection, and liquid chromatography (LC) with high resolution MS/MS detection. GC peaks with the correct …

Course Portfolio: Csce 478/878 Introduction To Machine Learning, Hongzhi Guo

UNL Faculty Course Portfolios

This course portfolio was developed for CSCE 478/878: Introduction to Machine Learning, offered by the School of Computing at the University of Nebraska–Lincoln, as part of the Faculty-led Inquiry into Reflective and Scholarly Teaching (FIRST) project. The course provides an introduction to foundational machine learning concepts, models, and programming tools. It enrolls both undergraduate and graduate students from a wide range of academic backgrounds, including computer science, computer engineering, software engineering, biological systems engineering, chemical engineering, business-related fields, etc. Offered in Spring 2025, the course was peer-reviewed to support continuous improvement in teaching. The primary goals of this portfolio are …

Historical Overview, Advancements, And Future Prospects Of Coalfield Structure Research In China, Cao Daiyong, Ju Yiwen, Xia Yucheng, Jiang Bo, Ning Shuzheng, Wei Yingchun, Wang Anmin, Li Xiaoming, Zhan Wenfeng, Li Huantong, Wang Lu, Cai Zhuocheng

Coal Geology & Exploration

Background Coalfield structures represent a basic aspect of coalfield geology. In China, research into this domain has a history of over a century, yielding numerous results after undergoing exploration, learning, and innovation. These results have played a positive role in the long-term, stable coal supply in China. Amidst the ongoing energy structure optimization and the transformation and upgrading of coalfield geology in China, it is both practically and historically significant to review the history of coalfield structure research, summarize relevant experience, and contemplate future directions of the research under emerging circumstances.Methods Based on literature analysis, as well as the …

Classification-Based Point Cloud Denoising And 3d Reconstruction Of Roadways, Chen Denghong, Pang Ning, Nie Wen, Feng Juqiang, Kan Jiliang, Zhang Jinjing

Coal Geology & Exploration

Objective The point cloud denoising and 3D reconstruction of roadways serve as a key step in the digital modeling and analysis of roadways. However, the conventional algorithm based on single filtering fails to effectively remove the noise at varying scales from point clouds. Meanwhile, the existing 3D reconstruction algorithms suffer from low modeling accuracy and high susceptibility to distortion. These necessitate developing methods and technologies to obtain high-quality point cloud data and construct high-accuracy 3D models for roadways. Methods This study proposed an adaptive classification-based point cloud denoising algorithm using neighborhood radius (R), minimum neighborhood point number ( …

Characteristics And Control Modes Of Geothermal Fields In Deep Coal Reservoirs In The Linxing-Shenfu Block, Xu Lifu, Bai Yuhu, Xu Bingxiang, Hu Weiqiang, Chen Jianqi, Li Yong

Coal Geology & Exploration

Objective Geothermal fields serve as a crucial geological condition controlling the phases and seepage of deep coalbed methane (CBM). Investigating geothermal fields facilitates the assessment and efficient development of deep CBM resources.Methods This study explored the Nos. 8 and 9 coal seams in the Linxing-Shenfu block using data from drilling, well logging, well tests, and experiments. Specifically, this study summarized the regional distribution patterns of geothermal fields in deep coal reservoirs in the block, analyzed the impacts of the geothermal fields on the gas-bearing and physical properties of the reservoirs, and revealed the controlling factors and mechanisms of the …

Dynamic Evolutionary Patterns Of High-Temperature Steam Seepage And Thermal Strains Of Coals During Heat Injection, Li Zhiqiang, Zhang Ningchao, Chen Jinsheng, Chen Liwei, Feng Wenxiang, Liu Longwei, Wang Yungang

Coal Geology & Exploration

Background Thermal stimulation of coal seams using high-temperature steam is recognized as a highly promising technology used to increase gas production. Steam permeability serves as a critical parameter for characterizing the injection capacity of thermal fluids. However, the seepage patterns and evolutionary mechanism of steam in coals remain unclear, and exploring these issues holds critical scientific significance for gas production enhancement via heat injection. Methods Through experiments on the seepage and thermal strains of coals during the injection of high-temperature steam performed using the steady-state method, this study investigated the dynamic time variation patterns of the steam permeability and thermal …

Fluoride Ion Removal From Mine Water Via Nucleation Crystallization Pelleting Process, Zhang Xiyu, Dong Shuning, Wang Hao, Jin Pengkang, Wang Xiaodong, Wang Qiangmin, Zhang Tao

Coal Geology & Exploration

Objective The mine water associated with coal mining tends to be rich in fluoride ions. If discharged directly without effective treatment, such water will cause severe pollution to regional ecology, affecting the quality of water resources and the stability of the ecosystem. Methods This study focuses on the challenging treatment of the fluoride pollution caused by coal mining-associated mine water. To overcome the bottlenecks including low efficiency and weak anti-interference of traditional methods for fluoride removal, this study designed a setup for fluoride removal using the nucleation crystallization pelleting (NCP) processing and proposed a novel fluoride removal method—NCP chemical precipitation. …

A Method For Efficient Virtual-Physical Synchronization Of The Digital Twin System Of An Excavation-Supporting Robot Cluster Targeting Coal Pillars Between Mining Faces, Mao Qinghua, Sima Junlei, Ma Hongwei, Wang Chuanwei, Chen Yanzhang, Guo Wenjin, Cui Wenda, Cheng Jiashuai

Coal Geology & Exploration

Objective Virtual models for excavation-supporting robot clusters targeting coal pillars between mining faces encounter challenges like a large data size and anomalies in data transmission, which lead to poor virtual-physical synchronization. This study proposed a method for efficient virtual-physical synchronization of the digital twin (DT) system of an excavation-supporting robot cluster using 3D model lightweighting and a trajectory prediction and correction model. Methods Fit-controlling vertices were defined, and their collapse cost factor was introduced to improve the quadratic error metric (QEM) algorithm and to constrain the lightweighting process of the 3D model of an assembly while maintaining fits between components. …

An Improved Mp-Nmo Correction Method Based On Multiparameter Fitting And Its Application, Yang Xiaohui, Yu Pengfei, Zhang Jiawei

Coal Geology & Exploration

Objective and Methods Normal moveout (NMO) correction is a core step in seismic data processing. However, the conventional NMO correction method tends to cause wavelet stretching when processing wide-angle or large-offset seismic data, leading to degraded stacking quality and distorted amplitude variation with offset (AVO) attributes. Although the matching pursuit (MP)-NMO correction method can mitigate the stretching effect, it will cause lateral discontinuity due to overlapping seismic events. Therefore, this study developed an improved MP-NMO correction method based on multiparameter fitting. Specifically, during MP decomposition, cubic polynomial fitting was applied to the amplitude, frequencies, and phases of wavelets from the …

Endo Almost 3–Absorbing Sub-Modules (Modules) And Related Concepts, Wafaa H. Hanoon, Mahmood S. Fiadh, Marwah W. Allami

Iraqi Journal for Computer Science and Mathematics

The concept of Endo Almost 3-Absorbing sub-modules (modules) is presented in this study, along with observations and the connections between Endo 2-Absorbing sub-modules (modules), Endo Approximately 2-Absorbing sub-modules (modules), Endo quasi-prime sub-modules (modules), and Endo prime sub-modules (modules). The study provides a range of attributes, illustrations, and justifications for these concepts. We aim to utilize the ramifications of this research to develop new ideas based on Endo Almost 3-Absorbing sub-modules (modules). Along with observations and an exploration of the relationships between Endo 2-Absorbing sub-modules (modules), Endo Approximately 2-Absorbing sub-modules (modules), Endo quasi-prime sub-modules (modules), and Endo prime sub-modules (modules), the …

Retracted: A Group Decision-Making For Selecting Multi-Deep Face Recognition Models, Abdulbasit Alazzawi, Qahtan M. Yas, Burhan Albayati

Iraqi Journal for Computer Science and Mathematics

Deep face recognition is a significant area of biometric authentication that addresses challenges such as low resolution, varying facial expressions, and inconsistent lighting. This paper presents a robust deep-learning approach to tackle these challenges. The study aims to employ multi-criteria decision-making techniques and verify the influence of individual and group expert opinions in decision-making. However, balancing criteria such as accuracy, sensitivity, specificity, precision, and recall remain challenging across different models. To fill this gap, the study utilized a decision-support framework that included the fuzzy analytical hierarchical process to set criteria weights based on expert input and the Technique for Order …

Ecg-Based Biometric Key Generation Using Principal Component Analysis And Machine Learning, Ahmad Abadleh

Iraqi Journal for Computer Science and Mathematics

Biometric authentication techniques are fast becoming imperative methods for secure identifications in a wide range of applications, while most of the traditional systems are easily spoofed or forged. In this paper a new approach of deriving a unique biometric key from an electrocardiogram signal is presented owing to physiological uniqueness of heart activity. In this respect, by focusing on RR intervals extracted from ECG signals, the PCA is applied in order to reduce its dimensionality and then form a compact and distinctive biometric key. Thereafter, a Random Forest classifier was used in evaluating the effectiveness of features, where a high …

Retracted: Robust Security System: A Novel Facial Recognition Optimization Using Coronavirus-Inspired Algorithm And Machine Learning, Saif Mohanad Kadhim, Johnny Koh Siaw Paw, Yaw Chong Tak, Shahad Thamear Abd Al-Latief

Iraqi Journal for Computer Science and Mathematics

Facial recognition has become an invaluable and rapidly advancing technology that plays a crucial role in various daily applications. From identity authentication to video surveillance, mobile payment, and even law enforcement and security measures. Despite the remarkable progress, facial recognition is still a dynamic research field and confronts several challenges. One of the main challenges is the high variability in facial images due to factors like facial expressions, lighting conditions, aging, and the presence of accessories. Additionally, the computational complexity and the time concerns surrounding face recognition systems have raised considerations that need to be addressed. This research presents a …

Retracted: Accurate Electrocardiogram Classification Of Heart Disease Using Deep Learning Network, Hadeel M. Saleh, Sahar Hamad Ahmed, Akeel Sh. Mahmoud

Iraqi Journal for Computer Science and Mathematics

Long short-term memory networks can effectively process complex temporal patterns in electrocardiogram data. These sequential models excel at classifying heart disease from the rich signals captured by electrocardiograms. However, traditional algorithms struggle with the intricate waveforms encoded in each heartbeat. Deeper architectures such as LSTM are better equipped to untangle the subtle variations between healthy sinus rhythms and lethal arrhythmias. In this study, an LSTM model was developed to diagnose disease from the PTB dataset. The network was trained using a fusion of deep learning schemes for sequential data. The model underwent several evaluations, from a confusion matrix mapping predictions …

Retracted: Intrusion Detection System For Iot Based On Modified Random Forest Algorithm, Omar Z. Akif, Sura Mazin Ali, Ann F. Sabih, Ahmed T. Sadiq, S. K. Subramaniam

Iraqi Journal for Computer Science and Mathematics

An intrusion detection system (IDS) is key to having a comprehensive cybersecurity solution against any attack, and artificial intelligence techniques have been combined with all the features of the IoT to improve security. In response to this, in this research, an IDS technique driven by a modified random forest algorithm has been formulated to improve the system for IoT. To this end, the target is made as one-hot encoding, bootstrapping with less redundancy, adding a hybrid features selection method into the random forest algorithm, and modifying the ranking stage in the random forest algorithm. Furthermore, three datasets have been used …

Retracted: Cipher Text To Secure Li-Fi System Using Hybrid Encryption Algorithm, Mohammed M. Ahmed, Satea H. Alnajjar

Iraqi Journal for Computer Science and Mathematics

It is important to pay attention to develop our security systems, due to the increase in cyberattacks and the development of their methods and the work to develop quantum computers capable of penetrating the solution of complex equations of encryption algorithms. The need to develop protection methods has emerged in line with the development of hackers to ensure the security and safety of users' data. In this research, work was done on the modern Li-Fi technology based on comparing the values with Kim's model and a distance of more than 13 km was reached in the fresh air. This technology …

A New Class Of Endo-R.B Module And Its Relationship With Modules, Mohammed Salman Murad, Buthyna Najad Shihab

Iraqi Journal for Computer Science and Mathematics

This paper gives a definition of a new class of T-module and T-submodule called an Endo-Restricted Bounded module (submodule) written shortly by Endo-R.B. module (submodule) and present some different approaches to connect this class of module with other types of modules such as: compressible modules, monoform modules, critically compressible modules, retractable modules, and quasi-Dedekind modules. One of the main purpose of this work is to introduce a few new conditions and reveal some properties and corollaries. This paper considered to be another solution or answer for Zelmanowitz’s problem. In fact, an Endo-R.B. T-module plays an important role to this problem …

The Impact Of Growth Hormone-Releasing Hormone Antagonist On Human Adipose-Derived Stem Cells, Jeanne Michelle Dugas

Master's Theses

Obesity is a condition that affects over 40% of American adults and contributes to numerous preventable health issues. This condition is caused by increased adipose tissue, which forms when stem cells transform into adipocytes. Mesenchymal stem cells, including human adipose-derived stem cells (hASC), are multipotent with the ability to differentiate into adipocytes, osteocytes, or chondrocytes. Many factors affect this transformation, including the peptide growth hormone-releasing hormone (GHRH). The main role of GHRH is to trigger the release of growth hormone, but it has also been found to play an important role in adipocyte metabolism. We expect that if the differentiation …

The Antioxidant Potential Of Rosemary Leaf-Derived Nanovesicles, Shani Akila Griffin

Master's Theses

Human dermal fibroblasts are essential for maintaining skin homeostasis. However, exposure to exogenous and endogenous stressors can lead to fibroblast instability, extracellular matrix degradation, and excessive reactive oxygen species (ROS) production. These factors contribute to skin aging and ROS-induced skin disorders leading to hair loss. Adverse effects from synthetic treatments have driven interest in natural alternatives like plant extracts for skin therapy. However, the polyphenols in these treatments require an effective delivery system to ensure their stability and targeted application. Plant derived nanovesicles (PDNVs) are emerging as a promising natural alternative for therapeutic applications. Their biocompatibility, stability, and encapsulated compounds …

Detection And Mitigation Of Out-Of-Band Channel Wormhole Attack In Wireless Network Using Propagation Delay, Harry May

Doctoral Dissertations

Wireless networks, susceptible to a range of attacks due to their simplicity and ease of evasion, face a significant threat from control data attacks, notably the elusive wormhole attack. Detecting and mitigating such attacks poses challenges, particularly in the absence of a digital signature. This dissertation introduces an innovative approach that utilizes the propagation delay associated with malicious nodes’ timing characteristics for detection, employing the Ad-hoc On-Demand Distance Vector (AODV) algorithm as its foundation. The inherent propagation delay in the AODV protocol is calculated for each node link along the entire communication path, offering a distinctive timing method that provides …

Dsa-Api: Data Standardization Automation Using Ai-Powered Apis, Andrew Asher Turner

Master's Theses

The common factor with current implementations of Artificial Intelligence (AI) is data. Companies are constantly looking for new ways to analyze data, but it comes in various formats: Text, Comma Separated Value (CVE), JavaScript Object Notation (JSON), Extensible Markup Language (XML), and Excel. How can AI be adapted to standardize formats for data analysis, integration, and digestion efficiently? Published research acknowledged that Machine Learning (ML) and AI can provide an automated method to speed up this process and limit the human decision-making error. With the advancement in AI, Application Programming Interfaces (APIs) prompt the idea that they can take in …

The Role Of A Lower Elementary Principal In Championing Co-Teaching As A Strategy To Promote Inclusive Education, Lauren Pigue Keen

Doctoral Dissertations

This qualitative autoethnographic study explores the role of a lower elementary principal in leading a co-teaching initiative to create a more inclusive educational environment for students with disabilities. Rooted in Lewin’s Change Theory, the study examines how personal experience, professional vision, and intentional leadership contributed to the implementation and expansion of co-teaching practices within an early childhood setting. The research focuses on the principal’s dual role as both a systems-level change agent and an insider experiencing the transformation firsthand. Data were collected through reflective narrative, stakeholder interviews, and document analysis to provide a comprehensive view of the leadership decisions, cultural …

Who Should Take Responsibility For Artificial Intelligence Actions And Outcomes? Perception Of Auditors As Users Of Ai Systems, Hanh Hoang Le

Doctoral Dissertations

As artificial intelligence (AI) systems become increasingly embedded in auditing processes, questions arise regarding how professional auditors perceive and allocate responsibility for AI-assisted decisions. This study investigates the effects of AI explainability and auditors’ perceived autonomy on perceived responsibility in the context of audit decision-making. Drawing on theories of moral responsibility and professional judgment, the study employs a 2x2 experimental design using hypothetical audit scenarios to manipulate levels of AI explainability and auditors’ autonomy. Hierarchical regression analysis reveals that perceived autonomy statistically significantly increases auditors’ perception of responsibility for AI-assisted decisionmaking, whereas AI explainability is not a significant predictor. Additionally, …

Competencies For Effective Stem Leadership: Essential Knowledge, Skills, Dispositions, And Practices To Inform Stem Education Programs And Support Program Leadership, Melissa Adams Wooley

Doctoral Dissertations

The purpose of this study was to identify a set of knowledge, skills, dispositions, and practices that are important for STEM leaders to consider as constructs of a STEM theory as they plan and implement a STEM program. The research design included a modified Delphi panel to gain consensus on the knowledge, skills, dispositions, and practices, called competencies, that effective STEM leaders consider when planning and implementing a STEM program. The panel participants were experts in STEM education. Based on the literature, the researcher identified the initial set of competencies. The panel identified ten additional competencies through the Delphi process, …

Encryption With Synchronized Chaos Using Fabricated Cobalt Ferrite Memristors, Kiran Sai Seetala

Doctoral Dissertations

No abstract provided.

Why Throughput Is Group-Put, Edward Sales

Doctor of Nursing Practice Final Manuscripts

Introduction: University of California San Francisco (UCSF) Saint Mary’s Health, has two hundred forty licensed beds to utilize for patient care, ninety one that can be used for inpatient medical surgical patients. Throughput is a challenging issue, especially at Unit seven west which houses thirty beds. Post op surgical orthopedic patients, medical surgical patients from the emergency department (ED), intensive care unit (ICU), and telemetry downgrades are waiting for inpatient beds, which causes delays in care and increases length of stay.

Background: Unit seven west is a surgical ortho floor with a high admission census consisting of three to ten …

Suprathel Burn Dressing Versus Standard Care: Impact On Hospitalization Duration And Post- Discharge Outpatient Visits, Isaac Awolola

Doctor of Nursing Practice Final Manuscripts

The aim of this evidence-based project is to determine the effectiveness of Suprathel a synthetic, one-time application, wound dressing that acts as a temporary skin substitute, particularly effective for partial-thickness burns and deep dermal burns vs, the standard of either Polysporin or Santyl, Xeroform, gauze on the frequency of outpatient (OP) visits after a patient’s discharge. A secondary aim of this project was to investigate whether Suprathel burn dressing significantly decreases the length of stay (LOS) for patients with second-degree burns involving < 20% total body surface area (TBSA). The results showed that the use of Suprathel dressings resulted in a shorter hospital stay 7.05 vs 9.35 days with a higher number of outpatient visits 5.47 vs 2.69 visits, compared to conventional dressings. The use of Suprathel dressings as a standard practice reduces inpatient hospitalization but increases outpatient visits and resource utilization. The use of Suprathel dressings shortens the length of hospitalization and may augment patient comfort, and decrease overall healthcare costs associated with hospitalization. Due to the reduction in need for painful daily dressing changes, Suprathel dressings constitute a potentially preferred option in burn care and could thereby influence treatment protocols and enhance patient outcomes.

Keywords: Wound care, Suprathel dressing, total body surface area, Outpatient Visits, Second-degree Burn, Length of stay, Gauze

Passive Vibration Isolation Systems For Mitigating Rotational Forces In Football Helmet Impacts, Julian Lee

LSU Master's Theses

Helmets have been used for centuries to mitigate head injuries, evolving with advancements in materials and design. They are critical in athletics, construction, and military applications, where shock and vibration isolation play a key role in head protection. Helmet performance is currently rated using the Head Injury Criterion (HIC) and Helmet Performance Score (HPS) metrics. HIC is a metric used in sports and automotive industries which focuses on the probability of injury occurring due to the magnitude and duration of linear acceleration of the head [1]. The HPS metric was created by using a linear combination of HIC and a …

Leading Change For Transgender And Gender Diverse Healthcare: Assessing Organizational Readiness, Braxten Lovett

Dissertations

Background: Transgender and gender-diverse (TGD) individuals face persistent stigma and systemic discrimination across multiple societal domains, with healthcare settings—intended as safe spaces—often being among the most harmful. Despite increased visibility of TGD populations and growing research on their health inequities, inclusive healthcare remains insufficiently addressed by many healthcare leaders, adding to the urgency to advance equitable, inclusive care. Additionally, structural cisnormativity and binary gender frameworks continue to perpetuate misinformation, erasure, and health disparities. Addressing these entrenched barriers requires systemic reform rooted in cultural humility, inclusive practices, and a commitment to dismantling cisnormative bias within healthcare delivery and policy. This study …