Digital Commons Network^™

Mechanical Anisotropy And Depth Effects Of Deep Coal Seams, Mei Xiaofan, Wang Cong, Wang Kaifeng, Tang Shuheng, Zhang Songhang, Xi Zhaodong

Coal Geology & Exploration

Objective The mechanical properties of coal seams determine the stability, fracability, and fracturing performance of deep coal reservoirs. Since the in situ stress applied to coal increases with burial depth, deep coal differs from its shallow counterpart in damage behavior, key mechanical parameters, and failure mechanism. Furthermore, deep coal exhibits pronounced mechanical anisotropy.Methods Focusing on the No. 9 coal seam in the Linxian area along the eastern margin of the Ordos Basin, this study carried out triaxial compression tests at 60℃ under different confining pressures to determine the mechanical anisotropy of deep coal. By comparing the full stress-strain curves, …

An Improved Brittleness Characterization Method For Deep Coalbed Methane Reservoirs And Its Application To Well Logs, Huang An’Er, Gong Fei, Peng Suping, Zou Guangui, Zhang Zhaoji, Wu Yiquan, Rong Zhihong, Li Qingrun

Coal Geology & Exploration

Background Deep coalbed methane (CBM) reservoirs are characterized by high pressure, low porosity, and low permeability. Therefore, large-scale volume fracturing is required for their commercial development. As an important engineering sweet spot parameter for selecting optimal fracturing targets, the brittleness index can reflect the difficulty in generating complex fracture networks during fracturing. However, existing brittleness characterization methods are largely applicable to strata with high brittle mineral content, such as shales and sandstones, while methods for characterizing the brittleness of deep CBM reservoirs with high organic matter content and low brittle mineral content are lacking. Methods Rock physics experiments on 20 …

A Fault Identification Method For Deep Coalbed Methane Reservoirs Based On An Improved Transunet, Gao Congshun, Wu Haibo, Liu Lei, Wang Zijian, Huang Xuanyu, Jia Lilan, Xu Aiguo

Coal Geology & Exploration

Objective Small-scale faults in deep coalbed methane (CBM) reservoirs significantly affect CBM enrichment and the deployment of development wells. However, deep seismic data tend to suffer from low signal-to-noise ratios (SNRs) and poor continuity of reflection events, while conventional methods are likely to miss small-scale faults. To address these limitations, this study proposes a fault identification method based on an improved TransUNet. Methods The proposed method is developed based on the R50-ViT-B_16 TransUNet architecture. A coordinate attention (CA) module is introduced before the skip connections enter the decoder to enhance the feature representation of the locations and directional continuity of …

Petrophysical Model-Driven Seismic Inversion For Physical Property Parameters Of Deep Coalbed Methane Reservoirs, Chen Yang, Guo Qiang, Ba Jing

Coal Geology & Exploration

Objective Deep coalbed methane (CBM) reservoirs typically exhibit complex pore-fracture structures, as well as strong nonlinear and non-unique corresponding relationships between elastic and physical property parameters. In this context, this study develops a seismic petrophysical inversion method that accounts for pore structure variations. Methods This study investigated deep CBM reservoirs along the eastern margin of the Ordos Basin. First, a petrophysical model based on variable pore aspect ratios was established, and the corresponding linearized forward operator was derived. Then, the pore aspect ratios were estimated using near-borehole log data. Meanwhile, the multimodal prior distributions of reservoir physical property parameters were …

Intelligent Identification Of Deep Coal Macrolithotypes Based On Weighted Ensemble In Machine Learning: A Case Study Of The Shenmu Area, Ordos Basin, Yao Yuhao, Yang Zhaobiao, Huang Daojun, Wang Jianan, Dong Guodong, Chen Yilin, He Jinxian, Zhang Shang

Coal Geology & Exploration

Objective Coal macrolithotype represents a key geological property in the exploration and recovery of deep coalbed methane (CBM) resources, and its accurate identification is of great significance for selecting the optimal geological and engineering sweet-spot intervals. However, conventional coal macrolithotype identification relies heavily on core observation, suffering from prohibitively high costs since core data are extremely valuable and thus feature a small sample size. Meanwhile, conventional log-based threshold methods face challenges in effectively characterizing the complex boundaries between coal macrolithotypes.Methods This study investigated the No. 8 coal seam of the Benxi Formation, Shenmu area, eastern Ordos Basin. Based on …

A Probabilistic Lithofacies Classification Method For Deep Coal Measures, Xu Aiguo, Zhang Pingsong, Wu Haibo, Li Shenglin

Coal Geology & Exploration

Background Deep coal measures contain multiple unconventional reservoir types, including coal, shale, and tight sandstone. Accurate lithofacies classification helps identify the resource and engineering targets of unconventional gas within these deep sequences, thus guiding category-based extraction and efficient development. However, conventional lithofacies classification methods largely rely on elastic parameters derived based on the isotropic-medium assumption or linear amplitude variation with offset (AVO) inversion while neglecting the pronounced vertical transverse isotropy (VTI) anisotropy of deep coal measures, severely limiting the lithofacies classification accuracy. Methods This study develops a probabilistic lithofacies classification method for deep coal measures. First, high-accuracy elastic parameters, including …

Multi-Round Diverting Fracturing Technology For Deep-Seated Thin Coal Seams And Its Application: A Case Study Of The G3-1 Well Group In The Yichuan Block, Ordos Basin, Li Shuguang, Nie Zhihong, Ji Liang, Zhou Changhui, Bai Kunsen, Huang Li, Zhen Huaibin, Xing Xuejie, Zhao Haifeng

Coal Geology & Exploration

Objective Deep coal seams in the Yichuan block of the Ordos Basin are characterized by small thicknesses, vertical multi-layer superimposition, and strong vertical and horizontal heterogeneities. When used to develop these coal seams, multi-stage horizontal well fracturing faces challenges such as uneven fracture propagation across various stages in the horizontal direction and a low production degree of multiple coal seams in the vertical direction, complicating commercial exploitation. To address these issues, this study proposes a multi-round diverting fracturing technology based on a vertical cluster well group. In this technology, vertical layered fracturing using vertical cluster wells is employed to achieve …

Scaling And Blockage In Deep Coalbed Methane Wells: Mechanisms And Countermeasures, Zhang Lei, Chen Ming, Zhen Huaibin, Zeng Wenting, Hao Pengcheng, Hou Wei, Yin Decai, Zhang Kang, Liu Chuanqing, Tian Zhida, Liu Qihu, Wang Qian, Xiong Wei, Sun Weiwei, Xia Fei, Wang Chunyan

Coal Geology & Exploration

Background The exploitation of deep coalbed methane (CBM) is accompanied by continuous pressure drawdown. In this case, scaling and blockage are prone to occur in systems where formation water with high total dissolved solids (TDS) and high-concentration CO₂ coexist. In the middle to late production stage, the blockage of both wellbore and flow pathways in reservoirs frequently leads to abrupt declines in gas production and bottomhole pressure or even induces the shutdown of natural flowing production, severely restricting the stable production of deep CBM wells. Methods This study investigated the Daning-Jixian block in the eastern Ordos Basin. Using multiple …

Technology Roadmap And Countermeasures For Lean Development Of Low-Grade Deep Coalbed Methane: A Case Study Of The Linxing-Shenfu Block, Ordos Basin, Wu Guang’Ai, Xing Xuesong, Wu Jianshu, Zhu Ying, Zi Jingyu, Wang Zhi, Hou Yanan, Cheng Yuxiong, Liu Xin

Coal Geology & Exploration

Background Accelerating deep coalbed methane (CBM) exploitation is a strategic initiative for the energy security and energy mix optimization of China. The Linxing-Shenfu block in the Ordos Basin holds considerable resource potential of deep CBM. However, this block exhibits typical geological characteristics of low-grade deep CBM, including the complete absence of free gas, gas content < 14 m³/t, reservoir permeability < 0.1 × 10⁻³ μm², and strong heterogeneity. These characteristics lead to low efficiency of conventional development models. Therefore, there is an urgent need to develop an efficient technical system tailored to these characteristics. Method To address the core contradiction between reservoir …

Numerical Simulation Of Stress Shadow Effect And Cluster Spacing Optimization For Multi-Cluster Fracturing In Deep Coalbed Methane Horizontal Wells, Guo Guangshan, Deng Zhiyu, Sun Le, Pu Yifan, Wu Jian, Tao Shu, Chen Shaojuan, Chen Shida

Coal Geology & Exploration

Purpose Multi-stage, multi-cluster fracturing in horizontal wells represents a key technique for deep coalbed methane (CBM) recovery and the sustainable development of the industry. However, during multi-cluster fracturing in deep CBM horizontal wells, the stress shadow effect tends to induce non-uniform fracture propagation and a limited stimulated reservoir volume (SRV). Methods To address these challenges. this study established a two-dimensional (2D) numerical model for multi-cluster hydraulic fracturing in horizontal wells using the finite element method. Then, it systematically analyzed the impacts of in situ stress and cluster spacing on both the stress shadow effect and the competitive propagation of hydraulic …

Fracturing With Three-Dimensional Full-Scale Fracture Network Propping For Deep Coalbed Methane Reservoirs: A Novel Ceramic Micro-Proppant And Its Verification Through Numerical Simulations, Li Xiang, Xu Jun, Li Ting, Zhang Xiaoyu, Yang Bo

Coal Geology & Exploration

Objective and Methods Conventional fracturing struggles to achieve the long-term effective propping of complex fracture networks during the development of deep coalbed methane (CBM) reservoirs. To address this challenge, this study developed a novel ceramic micro-proppant suitable for the volumetric microfracture propping and, through numerical simulations, verified its performance when it was applied to reservoir stimulation in conjunction with the technology of fracturing with three-dimensional full-scale fracture network propping. First, a novel ceramic micro-proppant with a core-shell structure was designed according to the requirements of microfracture propping. Its key properties, including compressive strength, settling behavior, hydraulic conductivity, and stability, were …

A Spatiotemporal Surrogate Model And Multi-Objective Optimization For Multi-Well Coalbed Methane Production, Qin Yuancheng, Wang Mengyi, Wang Yanfeng, Li Yuan, He Bo, Fang Junling, Duan Qingshan, Liu Chen

Coal Geology & Exploration

Objective and Methods In the full-lifecycle optimization of multi-well and long-term coalbed methane (CBM) production, conventional numerical simulation methods face challenges including extremely high computational costs and difficult multi-objective trade-offs. To address these issues, this study proposed a spatiotemporal surrogate model for multi-objective optimization of CBM production. First, a heterogeneous reservoir model involving 17 production wells was constructed based on high-fidelity numerical simulation, and a sample dataset was created by applying random perturbations to the baseline production scheme. Second, a hybrid surrogate model integrating a convolutional neural network (CNN) and a Transformer decoder was established. The CNN was utilized for …

Numerical Simulation And Process Optimization Of Multi-Round Fracturing In Deep Coalbed Methane Wells In The Linxing-Shenfu Block, Li Yu, Zhang Liang, Li Yunyun, An Qi, Feng Xuesong, Lan Tian, Yu Ji, Liu Xin

Coal Geology & Exploration

Objective Deep coalbed methane (CBM) holds considerable resource potential. However, deep CBM reservoirs exhibit high in-situ stresses, while deep coals feature strong plasticity and heterogeneity. These characteristics render it challenging to generate complex fracture networks through conventional fracturing, thus restricting the large-scale commercial development of deep CBM resources. Multi-round fracturing enables fracture initiation, propagation, and propping through sequential cycles of fracturing fluid injection, pump shut-in, and injection. By constantly changing near-wellbore stress fields, this technique facilitates the formation of complex fracture networks and ensures uniform reservoir stimulation. Method Using the finite-discrete element method (FDEM), this study established a numerical model …

Modeling Urban Heat Island Mitigation Strategies In Egyptian Cities Using Envi-Met, Islam M. Gaber, Abbas M. Hassan, Tarek Elkashef, Ola A. Gad, Ahmed M. Abdo, Abdelrazek Elkomy

HBRC Journal

Mitigation techniques for urban heat islands (UHI) have emerged as a critical concern for global urban environmental sustainability. Passive cooling employing cool materials and/or urban greening has shown to be the most effective strategy for mitigating the impacts of UHI. This latter method, however, is relatively new in the literature of urban environmental studies, and research on it is still limited, particularly for cities in hot-dry climatic zones. This study aims to investigate the possibility of improving albedo rates and adopting urban greening measures, such as greening building roofs, roadways, and walkways, in several Egyptian cities located in various climatic …

Multi-Solution Ternary Grover’S Algorithm For Logic-Based Quantum Machine Learning With Pseudo-Kronecker Reed–Muller Form Minimization, Sophia Lee, Ali Al-Bayaty, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

Optimization problems in machine-learning (ML) applications can be computationally challenging for classical methods, particularly when they involve large, unstructured search spaces. Quantum computing offers a promising approach to combinatorial optimization by utilizing quantum superposition and amplitude amplification. This paper presents a new methodology using logic-based quantum machine learning (QML) as a complete framework for employing a multi-solution ternary Grover’s algorithm to minimize incomplete binary functions represented by Pseudo-Kronecker Reed–Muller (PKRO) expansions, consistent with Occam’s razor principle. Unlike previous quantum approaches based on Kronecker Reed–Muller (KRO) or fixed-polarity Reed–Muller (FPRM) representations, our work introduces the first Grover-based optimization framework for PKRO …

Retraction Notice To "Hydrogen Recovery, Cleaning, Compression, Storage, Dispensing, Distribution System And End-Uses On The University Campus From Combined Heat, Hydrogen And Power System" [International Journal Of Hydrogen Energy 39 (2014) 647-653], Tarek A. Hamad, Abdulhakim A. Agll, Yousif M. Hamad, Sushrut Bapat, Mathew Thomas, Kevin B. Martin, John W. Sheffield

Engineering Management and Systems Engineering Faculty Research & Creative Works

This article has been retracted: please see Elsevier policy on Article Correction, Retraction and Removal (https://www.elsevier.com/about/policies-and-standards/article-withdrawal). This article has been retracted at the request of the Editor.

Harnessing Nigeria’S Aquaculture And Fisheries Component Of The Blue Economy, B. S. Awe, O. Adeoti

Blue Economy

Nigeria aims to diversify from carbon to blue economy. Target 7 of Goal 14 of the United Nations 2030 Agenda for Sustainable Development, the creation of the Federal Ministry of Marine and Blue Economy, and the passage of the National Policy on Marine and Blue Economy (2025 -2034) in May 2025 are the three frameworks driving this ambition. However, water pollution and changes in climate are central to harnessing the aquaculture and fisheries component of the blue economy. With increasing waste disposal in waterways and waterbodies and decreasing river water flow due to changes in climate, these have implications for …

Improving Mechanical Properties Of Soft Clay By Deep Soil Mixing In North Delta, Egypt Vs. Finite Element Simulation, Ali Gamal Abdel Zaher, Sherif Abdelaziz Mazek, Samir Abdelfattah, Mostafa Merzk

HBRC Journal

Soft clay layers in the Northern Nile Delta pose significant geotechnical challenges due to low shear strength, high compressibility, and susceptibility to settlement. This study implements a section of a canal embankment in the field using Deep Soil Mixing (DSM) with a cement content of 165 kg/m³ and aims to evaluate its effectiveness in stabilizing soft clay while providing practical design tools for similar deltaic environments. Long-term field monitoring and laboratory testing, including UCS and permeability measurements, were conducted to support the analysis. A Finite Element Model (FEM) was developed in PLAXIS 2D v21 and calibrated against field data, …

Safety And Resilience In Disaster Debris Removal And Clean-Up Operations: A Field And Training Guide For Resilience Workers In Disaster Debris Removal And Cleanup, Mohsen Shahandashti, Ph.D, P.E., Abhijit Roy, Ph.D, Zhe Yin, Ph.D, P.E., Pmp, Leed Ap, Antonio Balderrama, Ph.D, P.E., Sandesh Adhikari, Santosh Acharya

Mavs Open Press Open Educational Resources

A practical safety guide for the people who clear debris after disasters, and for the trainers who prepare them. Built for crews of every experience level--from seasoned operators to first-time volunteers--it teaches how to read a damaged site, recognize hidden and shifting hazards, work safely around heavy equipment and hazardous materials, and make sound decisions under pressure. Every chapter pairs clear guidance with a one-page field job aid, facilitator notes for trainers, and a real-world scenario, all grounded in OSHA, FEMA, EPA, and NIOSH guidance.

Safety and Resilience in Disaster Debris Removal and Clean-Up Operations Copyright © 2026 by Mohsen …

Integrated Fire Protection Design In Pharmaceutical Manufacturing Facilities: An Applied Case Study, Eman Osman Mahmoud Osman, Fouad Abdel-Mawgoud Abdel-Halim Qasim, Alaa El-Din El-Sayed Farid Hassan

HBRC Journal

The architectural design of pharmaceutical manufacturing facilities presents a complex engineering challenge, requiring simultaneous compliance with Good Manufacturing Practice (GMP) containment standards and life safety egress codes. This research presents an applied methodology for integrated fire protection design in sterile pharmaceutical environments, demonstrated through a case study of a manufacturing complex comprising cleanroom production zones and high-bay storage warehouses with heights reaching 14.8 m. The study reconciles facility classification under the Egyptian Fire Code and NFPA standards by defining Occupancy Hazard Groups W-1 and W-2 (high- and moderate-hazard storage), while utilizing regulatory Code Tables C-1 and C-2 to calculate occupant …

Cognitive Resilience At The Edge: Hyperdimensional Computing Versus Deep Learning For Hardware-Degraded Rf Classification, Adrian B. Cisneros, Jeong Yang

Military Cyber Affairs

Autonomous Collaborative Combat Aircraft (CCA) operating in contested electromagnetic environments must classify Radio Frequency (RF) signals on edge silicon that degrades over the mission lifetime due to thermal stress, radiation, and manufacturing variation. Deep neural networks dominate RF classification on pristine hardware, but their weights are precise and interdependent, causing catastrophic accuracy collapse as the underlying chip ages. We investigate whether Hyperdimensional Computing (HDC), a brain-inspired paradigm that distributes information across thousands of dimensions, can provide a reliability floor where Deep Learning fails. Using the RadioML 2016.10A dataset filtered to five digital modulations relevant to drone command-and-control links, we trained …

Hands-On Ransomware: An Experiential Wannacry Case Study For Undergraduate Cybersecurity Education, Eli Creek Richmond, Thomas R. Devine

Military Cyber Affairs

Ransomware represents one of the most disruptive threats in the cyber landscape, yet hands-on malware analysis remains rare in undergraduate cybersecurity curricula. This paper presents the design, implementation, and evaluation of an experiential learning module centered on the WannaCry ransomware case study, deployed in a senior-level course at West Virginia University. Students performed static and dynamic analysis using industry-standard tools. Pre- and post-module assessments demonstrated measurable gains in self-reported competency across seven technical dimensions. The module's competencies align directly with DoD Cyber Workforce Framework Work Role 212, Cyber Defense Forensics Analyst, supporting education-to-workforce pipeline development.

Breaking The Build: Detecting Software Supply Chain Vulnerabilities In Ci/Cd Pipelines, Mercedes R. Wahl, Dr. Benjamin Yankson

Military Cyber Affairs

This study examines whether integrating structured DevSec- Ops security controls into CI/CD pipelines can reduce software supply chain risk by preventing vulnerable components from progressing through the software development lifecycle. Software supply chain attacks frequently originate from weaknesses or compromises within dependencies, build environments, and trusted development stages, making early detection essential. A controlled sandbox experiment compared two pipeline configurations: a baseline CI/CD pipeline with no automated security enforcement and a secure DevSecOps pipeline integrating automated vulnerability scanning, SBOM generation, and artifact integrity verification. A known vulnerable dependency, the Python requests package (version 2.19.0) associated with CVE-2018-18074, was intentionally introduced …

Mitigating Common Vulnerabilities And Exposures In Cobol-Based Critical Systems Using The Strangler-Fig Pattern, Lauren E. Caruso, Vincent J. Compeau, Assefaw H. Gebremedhin

Military Cyber Affairs

COBOL-based legacy systems continue to underpin critical infrastructure in banking and government sectors despite their age and associated cybersecurity risks. Originally developed through a Department of Defense–sponsored initiative to standardize business computing, COBOL remains widely used in mission-critical environments. However, these systems face increasing vulnerabilities due to outdated security architectures, workforce shortages, and rising maintenance costs. This paper examines cybersecurity and operational challenges associated with COBOL systems and evaluates the Strangler Fig pattern as a modernization strategy that enables incremental replacement while maintaining continuity. The findings highlight implications for financial institutions and public-sector organizations dependent on legacy infrastructure.

From Framework To Toolchain: Implementing Zero Trust Architecture In Cloud-Native Environments For Dow Compliance, Shelby C. Snyder

Military Cyber Affairs

Federal agencies face a fiscal year 2027 target for enterprise-wide Zero Trust deployment, but NIST SP 800-207A defines logical components without identifying the Kubernetes technologies that implement them. This paper proposes a three-tier mapping of the Policy Engine, Policy Administrator, and Policy Enforcement Point to service mesh, microsegmentation, and perimeter tooling, stating the criteria by which each component is classified. It then applies a defined rubric to six Zero Trust vendors across component alignment, Kubernetes capability, federal authorization posture, and evidence quality, finding that no single vendor covers all three tiers. The mapping is a testable architectural proposition; a Stage …

Hybrid Deep (Cnn-Bilstm) Intrusion Detection For Defense And Mission-Critical Networks, Corey A Cheng, Jermaine Anim-Addo, Asma Jakir Hussain, Zion O Smith-Fox, Sanjay Goel, Yuksel Celik

Military Cyber Affairs

This article examines how hybrid deep learning can strengthen intrusion detection for military and defense networks. Using the CSE-CIC-IDS2018 dataset, the study evaluates a CNN-BiLSTM model designed to detect benign traffic and multiple attack categories, including DDoS, DoS, botnet, brute-force, web attack, and infiltration activity. The model achieved strong multi-class detection performance, with 0.9893 accuracy and 0.9979 ROC-AUC. The findings suggest that AI-supported intrusion detection can improve cyber defense operations, analyst triage, and protection of mission-critical networks.

Are Large Language Models Safe? A Vulnerability Analysis Of Generated Source Code, James Richards-Perhatch, Mitchell Milander, James M. Halvorsen, Assefaw Gebremedhin

Military Cyber Affairs

The increasing complexity of software and demands for rapid deployment have pushed the software industry to rely more on large language models (LLMs) in developing source code. However, as this technology is still relatively recent, questions can arise about the safety of the generated code. This paper presents an analysis of seven LLMs with respect to the presence of vulnerabilities within source code. Our findings show that LLMs are more likely to produce vulnerable web applications than vulnerable C programs, and that vulnerabilities are more likely to occur when program size and complexity increases.

Semantic Shields: Automating Critical Infrastructure Defense Via Nlp-Driven Ransomware Profiling, Henry Trowbridge, Ian Zalcberg, Ryan Schley, Carter Yagemann, Natasha Phan, Srikar Maduposu, Vimal Buck

Military Cyber Affairs

Ransomware poses a growing threat to critical infrastructure, where successful attacks can disrupt operational technology (OT) and industrial control systems (ICS) with significant public safety consequences. However, attributing ransomware incidents to specific threat actors remains challenging due to ransomware-as-a-service ecosystems, actor rebranding, and the obfuscation of traditional indicators of compromise. This paper presents Semantic Shields, an NLP-driven attribution framework that leverages BERT-generated semantic embeddings and DBSCAN clustering to profile ransomware actors through the linguistic characteristics of ransom notes. Using a dataset of 295 ransom notes from 189 distinct threat groups, the framework achieved an 87.2% true positive clustering rate and …

Llm-Generated Countermeasures For Iot Cyberattacks, James Alger, Michael Tu

Military Cyber Affairs

The rapid expansion of the Internet of Things (IoT) has introduced significant cybersecurity challenges, particularly for resource-constrained devices that traditional intrusion detection systems often fail to protect effectively. This paper proposes a novel, two-phase autonomous security pipeline designed to bridge the gap between probabilistic threat detection and deterministic network enforcement. The framework first utilizes a custom Time Series Transformer (TST) to classify multivariate network traffic and identify specific attack vectors, such as ransomware, SQL injections, and malicious file uploads. In the second phase, an agentic AI layer, comprising a locally hosted Large Language Model (LLM) orchestrated via LangGraph, processes the …

Foreward, Todd Arnold

Military Cyber Affairs

No abstract provided.