Digital Commons Network^™

A Reflected Radio Wave-Based Exploration Method For Goaves In Closed/Abandoned Mines And Its Application, Pang Yao, Wu Rongxin, Wang Jinguo, Hu Ze'an

Coal Geology & Exploration

Objective and Method The redevelopment and reutilization of closed/abandoned mines hold great research significance and practical production value due to their substantial number. In China, the secondary development and utilization of energy and spatial resources in such mines started late, rendering foundational theories and key technologies relatively underdeveloped. To investigate the developmental characteristics of underground spaces within goaves in closed/abandoned mines, this study proposed a reflected radio wave-based exploration method. Specifically, an observation system with a fixed transmitter-receiver (TR) spacing was deployed in rock roadways above/below or on both sides of the mining face in a closed/abandoned mine. Then, a …

Multiscale Fusion Aot-Gan Intelligent Filling Of Blank Strip For Formation Microresistivity Imaging Logging, Huang Luyi, Wang Fei, Kong Lingsong, Jiang Qishu

Coal Geology & Exploration

Objective Formation microresistivity imaging (FMI) images features incomplete borehole coverage and thus contain blank strips due to the gaps between electrode pads, as well as their pushing and attachment mechanisms. Traditional blank strip filling methods suffer from several limitations, including susceptibility to image distortion and difficulty in retaining information on fine-scale structures such as fractures. This study developed an aggregated contextual-transformation generative adversarial network (AOT-GAN) for blank strip filling, aiming to achieve high-precision and high-fidelity information reconstruction. Methods Initially, a high-quality dataset was prepared based on original FMI images, along with full-borehole images obtained through image inpainting using CIFLog. Then, …

A Drilling-Punching-Hole Protection Integrated Hydraulic Caving Technology For Broken Soft And Low-Permeability Coal Seams, Yao Ningping, Hao Shijun, Zhang Jinbao, Wei Hongchao, Nie Chao

Coal Geology & Exploration

Objective and Method For broken soft and low-permeability coal seams, gas drainage achieved through permeability enhancement and pressure relief by cross-seam hydraulic caving in coal seam floor faces problems including serious borehole collapse in the drilled coal seam intervals, low construction efficiency, and poor gas drainage performance. To address these problems, this study proposed a high-efficiency drilling-punching-hole protection integrated gas drainage technology, which enabled hydraulic caving, permeability enhancement, and pressure relief. Furthermore, this study developed a drilling-punching-hole protection integrated caving device. Through laboratory experiments conducted under pressure conversion and central channels equipped with controllable opening/closing mechanisms, this study selected the …

Cross-Modal Lidar-Camera Joint Calibration For Heading And Mining Face In Coal Mines, Yang Wenjuan, Ren Zhiteng, Zhang Xuhui, Du Yuyang, Li Changpeng, Zhang Chao, Xu Jie, Wan Jicheng, Lei Mengyu

Coal Geology & Exploration

Objective In underground coal mines, it is challenging to extract the features of the heading and mining face stably under insufficient illumination, significant dust interference, and sparse point clouds. This severely undermines the accuracy and robustness of camera-LiDAR joint calibration based on underground unstructured scene characteristics.Methods Targeting complex operating conditions, including fully mechanized mining face and tunneling roadways, this study developed a LiDAR-camera joint calibration method based on cross-depth feature coupling. In this method, an improved random sample consensus (RANSAC) algorithm for multi-plane fitting was designed for feature extraction. By integrating normal vector-based pre-clustering and an adaptive iteration mechanism, …

Note: Greenvent – Revolutionizing Methane Management With Hybrid Energy Processing In Mine Ventilation, Roman J. Jędrzejczyk, Damian K. Chlebda, Jacek Dańczak, Piotr Rosikowski, Monika A. Koperska, Robert E. Hayes, Joseph P. Mmbaga, Robert Hildebrandt, Joanna Profic-Paczkowska

Journal of Sustainable Mining

Methane plays a significant role in intensifying the greenhouse effect, possessing a global warming potential more than twenty times greater than that of CO₂ when measured on a carbon-dioxide-equivalent basis. Hence, identifying efficient methods to mitigate methane emissions is crucial for safeguarding the environment, ensuring economic viability, and maintaining practicality. Globally, research initiatives are dedicated to crafting an effective catalyst system for methane oxidation, which involves developing catalysts, comprehending their characteristics, and determining the best way of bonding them to structural supports to enhance functionality. Notably, systems that incorporate metals from the non-noble d-block of the periodic table as …

Uas Path Planning With Dynamic Rerouting Using A Space-Time Graph, Kimoy Williams

Electronic Theses and Dissertations 2020 - Present

UAS systems have emerged as multifaceted technologies with applications across a wide range of sectors. Their ability to access areas that are difficult or unsafe for manned systems has made them invaluable tools in various domains. As a result, UAS have transformed numerous industries, including infrastructure inspection, delivery and logistics, military and defense, as well as precision agriculture and environmental monitoring.

The advancement in UAS technology is fundamentally reliant upon ongoing research efforts in the specialized area of UAS path planning. Optimal flight planning is essential for a UAV to effectively execute its mission’s task safely, effectively, and in congruence …

“What You Bring Matters”: A Case Study Of Middle School Engineering Teachers’ Pedagogical Content Knowledge, Jessica D. Gale, Dyanne Baptiste Porter, Meltem Alemdar, Sunni H. Newton, Abeera P. Rehmat, Jasmine Choi

Journal of Pre-College Engineering Education Research (J-PEER)

Precollege engineering teachers bring unique backgrounds to their teaching practice. Many follow nontraditional routes to teaching engineering, often coming from teaching other subjects or careers in other fields. Among the many variations influencing engineering teaching practices is pedagogical content knowledge (PCK). This multiple case study explores the PCK of five middle school engineering teachers implementing the same middle school engineering curriculum, STEM innovation and design (STEM-ID). STEM-ID engages students in contextualized challenges that incorporate foundational mathematics and science and advanced manufacturing tools such as computer-aided design and 3D printing while introducing engineering concepts such as pneumatics, aeronautics, and robotics. Drawing …

A New Highly Oxygen-Deficient And Cubic Pr3Zro8-Δ For Intermediate-Temperature Thermochemical Production Of Oxygen And Hydrogen, Jiaxin Lu, Yongliang Zhang, Luhong Chen, Yan Chen, Ke An, Yasser Shoukry, Xinfang Jin, Zhi-Hao Wang, Sai Mu, Kevin Huang

Faculty Publications

Two-step thermochemical cycles offer a clean route for hydrogen and oxygen production but are typically limited to high temperatures exceeding 1500 °C. Lowering operating temperatures would enable the use of alternative heat sources such as industrial waste heat. Here, we report Pr₃ZrO₈ as a new enabling material for efficient intermediate-temperature redox cycling, with thermal reduction at 900 °C in argon and steam oxidation at 400 °C. Pr₃ZrO₈ adopts a face-centered cubic structure similar to CeO₂ but exhibits significantly greater oxygen deficiency, achieving average oxygen and hydrogen fluxes of 331.7 and 70.3 µmol·g-1, respectively, …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Enhancing Thermal Energy Storage Efficiency Using Optimized Concrete Materials: A Review, Ahmed Mohamed Abbass, Ahmed M. El-Khayatt, Khaled A. Eltawil

Mansoura Engineering Journal

The increasing global energy demand and the environmental impact of fossil fuels necessitate the development of sustainable energy solutions. Solar energy is a promising alternative, but its intermittency and inefficiency limit its widespread adoption. This study explores the role of advanced concrete-based thermal energy storage systems in addressing these challenges. Key aspects examined include packing density optimization, high-temperature performance, and hydrothermal durability of concrete. The research highlights the importance of granulometric optimization, binder selection, and hydration mechanisms in enhancing concrete properties. Additionally, the effects of heat exposure on concrete durability, permeability, and mechanical strength are analyzed, with a focus on …

Optimized Thermal Management Of Cylindrical Lithium-Ion Batteries Using Circular Fins And Forced Air-Cooling, Mohamed M. Donia, Ahmed A. Hegazi, Mohamed A. Aziz, Osama Abdelrehim

Mansoura Engineering Journal

The Battery Thermal Management System (BTMS) is vital for maintaining the optimal performance, safety, and longevity of electric vehicle batteries by keeping their temperature within a desired range. This study proposes an optimized cooling solution for BTMS based on both experimental and numerical investigations. Experimental tests were performed on a single battery cell under discharging rates of 1C, 2C, 3C, and 4C using an electronic load, with thermal performance evaluated under natural convection. Numerical models simulating both forced and natural convection were developed using ANSYS 19.3, and a combined Computational Fluid Dynamics (CFD) and Response Surface Methodology (RSM) approach was …

Verification Of Offline Signature Based On A Hybrid Dense-Attention-Residual-Cnn Structure Optimized By Grey Wolf Algorithm, Zeyad T. Salim, Mahmoud M. Saafan, Eman M. El-Gendy

Mansoura Engineering Journal

Signature verification is a necessary vision task with prevalent use in securing realworld applications. Offline signature is a popular type that needs to efficacious approach to be checked due to the depending on the paper time and the written tool. The progress in intelligent algorithms participates in supporting simple way authentication-based signatures. In this research work, an offline signature verification (OSV) based on an optimized and costumed convolutional neural network(CNN) infrastructure is suggested. The CNN is costumed by integrating residual, dense, and attention mechanisms, then optimized by applying a grey wolf optimization(GWO) algorithm. The system is trained and tested with …

Assessing The Geomechanical Modelling Of Underground Reservoir For Co₂ Storage Trapping Mechanisms, Bonavian Hasiholan, Mohammed Ali Farea, Elhassan Mostafa Abdallah, Sami Abdelrahman M. Yagoub, Yasir Mukhtar

Mathematical Modelling and Numerical Simulation with Applications

Effective carbon dioxide (CO₂) storage is essential for mitigating climate change amid increasing global greenhouse gas emissions. This study investigates the influence of geomechanics on CO₂ storage performance within carbon capture and storage (CCS), focusing on structural, residual, and solubility trapping mechanisms using a fully coupled modeling framework. Two numerical models, with and without geomechanical effects, are developed to evaluate impacts on reservoir behavior, CO₂ migration, and trapping efficiency. Each mechanism is analyzed separately and within an integrated framework to assess their combined contributions. Results indicate that geomechanical coupling increases reservoir pressure, reduces CO₂ flow velocity, enhances migration control, and …

Exploring Noise Induced Extreme Events In Neuronal Oscillators Networks And Machine Learning Forecasts, Hariharan S Mr

Theses and Dissertations

This doctoral dissertation comprehensively investigates the underexplored phenomenon of noise-induced extreme events (EE). The word “extreme” is accompanied by an occurring “event” when the deviation is extreme or higher than that of regular occurrences. These extreme occurrences are rare, abrupt, sudden, and irregular, often causing a profound impact on the system and its surroundings. Tsunami, earthquakes, solar flares, and tornadoes are such events that do not occur often but still significantly cause damage to mankind. This thesis particularly focuses on EE in neuronal systems where sudden synchronization can trigger seizures, tremors, and strokes which serve as classic examples of such …

Social Cognition Simulation With Large Language Model-Driven Agents, Mingxin Zhang, Jinxuan Wu, Rui Zhu, Yunlong Wang, Wenjuan Meng, Zhe Liu, Xu Li, Xiaolei Chen, Yuxuan Liang, Yi Zheng, Xiangyang Xue

Journal of System Simulation

Abstract: With the continuous evolution of the capabilities of generative LLMs, their application in social cognition simulation is demonstrating paradigm-shifting potential. Traditional social simulation methods predominantly rely on static rules and simplified behavioral models, making it difficult to capture the dynamic evolution and cultural complexity of human social behavior. LLM-driven agents, equipped with contextual understanding and natural language generation capabilities, are emerging as novel tools for modeling social cognitive mechanisms, enabling the simulation of complex sociopsychological processes such as identity construction, value judgment, and intentional reasoning. This paper briefly introduced the technical foundations of LLMs and highlighted their suitability for …

Research On Chain-Of-Thought Technology For Situational Awareness Based On Modular Reasoning, Hongyuan Ji, Duzheng Qing

Journal of System Simulation

Abstract: To address issues such as insufficient intelligence of situational understanding in traditional simulation systems, a situational visual question answering dataset was constructed, and a modular reasoning framework was proposed. The SACoT was built, which, under a zero-shot setting, employed expert prompts to guide the model in task decomposition and multimodal information fusion, generating reasoning chains to enhance semantic cognition and interpretability and offering a scalable solution with low computation cost. Experimental results indicate that SACoT improves task allocation, enables models to focus on query-relevant image details, mitigates the fragmentation of chain-of-thought induced by multi-step reasoning, and reduces long-form …

Simulation Of Robotic Arm Ball-Catching Strategy Based On Curriculum Rl Of Transformer, Ziyao Zhang, Yunfeng Ji

Journal of System Simulation

Abstract: Method integrating the PPO algorithm with Transformer network architecture is proposed, and curriculum learning strategy is introduced to solve the difficult training convergence and low efficiency of traditional RL methods in complex and dynamic high-degree-of-freedom tasks such as robotic arm ball catching. The Transformer is employed to effectively capture the complex high-dimensional dependency between the robotic arm's state space, ball trajectory, and environmental physical parameters. Curriculum learning progressively increases catching difficulty by designing training tasks from simple to complex objectives. The experimental results show this method increases the ball-catching success rate by over 60% compared to the traditional …

Research On Uav Target Tracking Algorithm For Simulation Scenarios, Xinyi Li, Zhenfei Wang, Han Wu

Journal of System Simulation

Abstract: To address the need for automatic UAV tracking of moving targets in simulated experiments, this paper proposed a long-term automatic tracking method based on an improved channel and spatial reliability-aware tracker (CSRT) algorithm. The target edge features were detected using the Laplacian of guided filter (LOGF) through guided filtering and then fused with the histogram of oriented gradient (HOG) and color names (CN) features to enhance the algorithm's discriminative ability for the target. To evaluate the target state, the paper used average peak correlation energy and perceptual hash Hamming distance. When the target was occluded, the paper employed YOLOv8 …

Distributed Optimization For Integrated Energy Based On Multi-Agent Reinforcement Learning, Caixia Tao, Naikun Chen, Fengyang Gao, Jiangang Zhang

Journal of System Simulation

Abstract: To address the energy management and privacy preservation problems faced by the coordinated optimization of distributed integrated energy systems, a distributed coordinated optimization strategy based on the multi-agent proximal policy optimization algorithm was proposed. An energy management model was established under the MDP framework; the electrical and thermal heterogeneous energy characteristics were considered; a multi-region two-layer interaction mechanism was constructed. Under the framework of centralized training and decentralized execution, homomorphic encryption was utilized to avoid privacy leakage during the coordination process, while accurately quantifying individual contributions to mitigate the problem of variance explosion in multi-agent policy evaluation. In the …

Prediction Of Inflow Wind Field For Large-Scale Wind Turbines Based On Multimodal Hybrid Deep Learning, Jiheng Wang, Yang Hu, Ziqiu Song, Fang Fang, Jizhen Liu

Journal of System Simulation

Abstract: To address the demand for high-precision inflow wind field prediction in large-scale wind turbines, traditional CFD methods suffer from high computational costs and poor real-time applicability. This paper proposed a multimodal hybrid deep learning-based wind field prediction method. The proposed method took turbine operating parameters and far-range wind field images as inputs and generated short-range wind field images as outputs. By employing a U-Net-Transformer-GAN hybrid architecture, the model achieved multi-scale feature extraction, temporal dependency modeling, and highresolution wind field image generation. The vorticity transport equation and Kármán-Howarth turbulence statistics were incorporated as weak constraints to enhance physical consistency, while …

Llm-Driven Multi-Agent Social Network Simulation: Interdisciplinary Integration And Cutting-Edge Development, Jiting Li, Yi Sun, Yirong Wang, Yiqin Lin, Jun Jia, Gangsong Ding

Journal of System Simulation

Abstract: The breakthrough of LLMs has provided powerful tools for social network research, advancing multi-agent social network simulation into a new era. This review systematically examined recent progress in LLM-driven multi-agent social network simulation research through a integrated perspective of multi-disciplines such as artificial intelligence, psychology, communication studies, and sociology. A three-tiered research system, which has gradually formed in this field and encompassed micro-level individual behaviors, meso-level interactive relations, and macro-level system emergence, was summarized. At the micro-level, research focuses on individual human behavior simulation, and numerous studies are dedicated to developing human-like agents with complex cognitive and affective architectures …

An Adaptive Robot Path Planning Based On Improved Rea* Algorithm, Ling Zhu, Jing Li, Zhaohui Zhang

Journal of System Simulation

Abstract: In order to improve the computational efficiency and path smoothness in a robot's global path planning, an adaptive robot path planning strategy based on an improved unilateral rectangle expansion A*(REA*) algorithm was proposed. The robot's operational safety was ensured by setting a buffer around obstacles. A passable interval formed by unilateral rectangle expansion was used as the operation unit, and bidirectional alternating search was combined to enhance the path planning efficiency. Inspired by potential field theory, the evaluation function was optimized by introducing a vector form to achieve fast adaptive obstacle avoidance. A new path planning strategy was proposed …

Evolutionary Game-Based Analysis Of Responses To Hallucinations In Generative Artificial Intelligence, Qiang Yan, Qianyu Zhang, Na Wei

Journal of System Simulation

Abstract: The accelerated deployment of generative artificial intelligence, particularly large language models, has amplified the social risks of hallucinations, posing systemic threats to the credibility of the information ecosystem, the effectiveness of users’ cognitive decision-making, and the governance security in the public domain. Research primarily focuses on hallucination mitigation mechanisms at the technical level or the design of regulatory frameworks at the policy level, lacking a systematic theoretical analysis of the evolutionary logic of strategic interactions among the “large language models, users, and regulators” under conditions of bounded rationality. By introducing evolutionary game theory into the field of generative artificial …

Intelligent Air Combat Decision-Making Method Based On Bigru And Priority Dynamic Sampling, Zhengkun Ding, Jiaqi Liu, Junzheng Xu, Yuezhu Xu, Xingmei Wang

Journal of System Simulation

Abstract: Current multi-agent reinforcement learning algorithms suffer from low efficiency in utilizing experience data and difficulties in setting appropriate learning rates. To address these issues, this paper proposed a BiGRU multi-agent PPO with priority sampling and dynamic learning rate. The algorithm incorporated a BiGRU network to enhance the policy network's ability to model temporal information. A priority partial sampling mechanism was introduced to improve the utilization efficiency of high-value experience data. Additionally, an improved Adam optimizer with dynamic learning rate adjustment was employed to address the challenge of learning rate configuration. Simulation experiment results demonstrate that the algorithm significantly …

Knowledge-Enhanced Llm-Based Method For Regional Traffic Signal Control, Risheng Xu, Linyao Yang, Yuanqi Qin, Xiao Wang, Changyin Sun

Journal of System Simulation

Abstract: Adaptive traffic signal control (ATSC) is crucial for alleviating regional traffic congestion, yet it faces severe challenges in real-time response to unexpected events and global coordination. The DRL method relies on pure data-driven approaches, suffering from core limitations such as poor generalization, weak interpretability, and a lack of guidance from emergency disposal knowledge, which makes them difficult to meet the demands of complex traffic scenarios. A control system that integrates knowledge-driven and data-optimized approaches was proposed. The GraphRAG was used to construct a dynamic traffic knowledge graph, providing LLMs with real-time updated historical emergency disposal experience and road …

A Semantic Knowledge-Enhanced Assessment Method For Spectrum Effectiveness Of Low Earth Orbit Constellations, Yiqing Liu, Qiuyang Zhang, Chunyu Liu, Yao Xue, Zhiwei Wei, Yan Feng

Journal of System Simulation

Abstract: In order to scientifically assess the spectrum effectiveness of low earth orbit constellations, the problems of insufficient adaptability of the traditional assessment framework and inaccurate estimation of KPIs under sparse data conditions were solved, a semantic knowledge-enhanced assessment method for spectrum effectiveness of low earth orbit constellations was proposed. A comprehensive multi-level, all band, and multi-dimensional spectrum effectiveness assessment framework covering link level, system level, geographical level, and service application level was constructed. A KPI intelligent prediction agent model integrating semantic knowledge and machine learning was proposed to quantify text-like design parameters using SentenceTransformer, so as to rapidly …

Simulation Method For Multi-Crew Construction Processes Based On Large Language Model-Powered Agent, Yifan Wang, Bin Yang, Congjun Wang

Journal of System Simulation

Abstract: Traditional construction simulation methods typically rely on predefined rules or static scheduling mechanisms, making it difficult to simulate interactive decision-making under complex constraints by dynamically adapting multi-crew construction scenarios. As a result, workforce idleness caused by process dependencies and spatial occupation fails to be solved. A simulation method for multicrew construction processes based on a LLM-powered agent was proposed. The distributed crew agents endowed with construction scenario understanding and reasoning capabilities were constructed, as well as a centralized project manager agent. A “single-manager and multiple-crew” decision-making mechanism for multi-agent interaction in construction was designed. Autonomous decision-making and coordination mechanism …

Integrated Development Environment For Digital Test Applications Based On Cloud-Edge-End Architecture, Wenguang Yu, Qun Li, Hongjie Dang, Hao Chen, Ping Yang

Journal of System Simulation

Abstract: Digital test applications need to be constructed using the unified digital test development tool. After analyzing the features of digital test applications such as large-sample autonomous run, high computational efficiency requirement, and diverse task scenarios, this paper proposes the integrated development environment (IDE) for digital test applications based on cloud-edge-end architecture. The layered expandable architecture, the hybrid integration framework of multi-source heterogeneous models, and the cloud-edge-end collaborative deployment architecture are designed for the IDE of digital test applications. The IDE supports the rapid development, integration, and execution of digital test models and enables development of digital test applications on …

Research On System And Application Framework Of Tactical Wargaming Simulation Driven By Ai4s, Dayong Liu, Qisheng Guo, Zhiming Dong, Xuehuan Qiu, Zhuoli Liu

Journal of System Simulation

Abstract: Tactical wargaming simulation, as a crucial tool for combat analysis, simulation training, and equipment demonstration and test, has become a significant means for generating combat effectiveness. Integrating AI into simulation not only enhances simulation efficiency but also diminishes reliance on humans. To assist professionals engaged in tactical wargaming simulation in mastering AI application methods, fostering a systematic mindset, and understanding evolving trends, this paper provided a concise overview of the principles behind AI for science (AI4S). Subsequently, it conducted an analysis of AI4S's application effectiveness in tactical wargaming simulation, established an AI4S-driven wargaming simulation system, and elucidated its composition, …

Knowledge Closed-Loop Driving-Based Intelligent Game Confrontation Simulation, Quan Liu, Yu Wang, Linyue Liu, Hao Chen, Jian Huang

Journal of System Simulation

Abstract: For human-machine intelligence integration and collaborative intelligence enhancement, a “knowledge-model-data-knowledge” closed-loop paradigm for combat simulation is proposed to guide the design of a DRL-based game confrontation simulation architecture. By building a combat priori knowledge-guided DRL agent model, mining and analyzing the time series data of agent interactions generated during simulations, and extracting combat posterior knowledge that expands the cognition boundaries of commanders, the knowledge closed-loop driving mechanism for intelligent combat simulations is achieved. The experimental results indicate that the proposed mechanism can effectively endow the combat simulation system with intelligence growth capabilities, providing valuable reference for the deepening …