Digital Commons Network^™

Hydrodynamic Evolution Simulations And Safety Assessment Of The Deep Reinjection And Storage Of High-Salinity Mine Water, Xu Zhimin, Chen Weixiao, Sun Yajun, Zhang Jian, Chen Tianci, Liu Qi, Chen Ge, Hua Jingfan

Coal Geology & Exploration

Objective In some mining areas of North China-type coalfields, large volumes of high-salinity mine water produced during coal mining pose dual challenges: high treatment costs to meet discharge standards and the waste of mine water resources due to inefficient utilization before discharge. In recent years, the deep reinjection and storage technology for high-salinity mine water has provided a new approach to this issue. To achieve this technology, it is essential to understand the flow field evolution during mine water reinjection and address reinjection-induced safety problems. Methods This study investigated coal mine A in Jiangsu Province as an example. Using the …

Key Technologies For Artificial Intelligence-Based Inversion Of The Borehole Transient Electromagnetic Method In Coal Mining Areas, Fan Tao, Hao Yue, Zhang Peng, Li Ping, Zhao Zhao, Zhao Rui, Liu Qiang, Yan Junsheng, Song Xianzhe, Liu Borui, Chen Changyuan, Li Bo

Coal Geology & Exploration

Background Using directional boreholes, the borehole transient electromagnetic (TEM) method enables near-field excitation and the simultaneous reception of three components within a borehole. This method can effectively avoid interference from ferromagnetic materials in roadways, thereby significantly enhancing the detection accuracy and range. Therefore, this method is widely applied to the detection of underground concealed water hazards in coal mines. However, there is a conflict between the accuracy and efficiency of current inversion methods for borehole TEM data, and existing technologies are insufficient to simultaneously meet the demands for high efficiency and high precision in the detection of concealed water hazards. …

Development And Prospects Of Underground Intelligent Drilling Rigs For Coal Mines, Yao Ningping, Li Quanxin, Lu Qianhai, Peng Guangyu, Zhang Gang, Zhang Youzhen

Coal Geology & Exploration

Background In coal mines, underground intelligent drilling rigs serve as critical equipment for gas extraction and water hazard prevention and control. With increasing mining depth, extreme operating conditions, including elevated gas concentration and high in situ stress, pose challenges to conventional drilling modes. Progress This study presents a systematic review of the evolutionary history of intelligent drilling rigs in China from the 13th to the 14th Five-Year Plan, dividing it into three stages: semi-automated operation, whole-process automated operation, and initial intelligent drilling. In the semi-automated operation stage, technologies for remote control within a viewing distance and automatic loading and unloading …

Intelligent Path Planning For Drilling Arms In Rock Bolting In Coal Mines Based On An Improved Rrt Algorithm, Zhang Xuhui, Xie Yanbin, Lei Mengyu, Song Chengzhi, Dong Zheng, Wang Zeyao, Wang Tenghui, Tian Sihao, Zhang Qinhuan

Coal Geology & Exploration

Objective Against the background of intelligent coal mining, automated underground rock bolting has become a core demand in enhancing the tunneling efficiency and safety of roadways. However, the path planning of drilling arms suffers from a low degree of automation, cumbersome posture adjustment, low operational stability, and insufficient drilling accuracy in complex underground environments. Methods To address these issues, this study developed an intelligent path planning method for drilling arms in rock bolting in coal mines based on an improved rapidly-exploring random tree (RRT) algorithm. First, a goal-biased guidance strategy was adopted to narrow the sampling range, and an update …

Exploration And Practice Of Talent Cultivation System For System Modeling And Simulation, Shaoping Wang, Chao Zhang, Ni Li, Yong Cui, Yongjia Zhao, Quan Quan

Journal of System Simulation

Focusing on the cultivation of innovative talents in modeling and simulation of aerospace and safety-critical control systems and facing the problems and challenges in the education and teaching of talent cultivation for system modeling and simulation, this paper inherited the "red ambition, soaring far" spirit of simulation researchers in Beihang University, constructed an integrated bachelor, master, and doctoral course system empowered by the latest key technologies, designed open and collaborative aerospace teaching cases, independently developed a control system simulation teaching platform based on Chinese simulation software MWORKS, and constructed a hardware-in-the-loop simulation experimental verification system for C919 aircraft, to …

Exploration Of Talent Cultivation System And Practical Model For Simulation And Optimization Of Intelligent Manufacturing System, Xinyu Li, Zheng Duan, Liang Gao, Chunjiang Zhang, Peigen Li

Journal of System Simulation

To address problems such as the insufficient integration of science and education in the talent cultivation system for traditional manufacturing system simulation and optimization, the insufficient integration of industry and education in cultivation goals and approaches, and the lack of full-chain industrial-level practical cultivation means, a "1223" reform scheme for innovative talent cultivation in the intelligent manufacturing system was formed. Research and practice were carried out focusing on the talent cultivation system, cultivation approaches, and practical cultivation resources for the simulation and optimization of the intelligent manufacturing system. Significant outcomes were achieved in aspects of innovative talent cultivation, faculty and …

Exploration Of Online-Offline Integrated Practical Teaching Path Driven By Digital-Intelligent Simulation, Zhen Zuo, Dong Zhang, Zhi Wang, Zhongxin Li

Journal of System Simulation

To address the disconnection between theory and practice, industry and education, and scientific research and teaching in traditional practical teaching, this paper utilized digital-intelligent simulation to empower practical teaching, promoted the collaborative linkage between national-level science and education platforms and high-quality industrial research and development platforms, and constructed a vehicle-oriented "CAE simulation, autonomous driving simulation, and performance testing simulation" practical system. Guided by the constructivist learning theory, this paper innovated the practical model by integrating online and offline approaches, built a competency-oriented practical education ecosystem through competition-education integration, and formed a new "competency-led, industry-oriented, and project-driven" paradigm of …

Iterative Evolution And Innovation Of Simulation-Based Experimental Teaching In Software Engineering, Jun Guo, Yixian Liu, Lianbo Ma, Jian Liu, Chunyan Xu

Journal of System Simulation

In view of the structural disconnection between talent training and industry needs caused by the limitations of traditional computer experiment teaching in scenario authenticity, technological frontier, interdisciplinary integration, and student subjectivity, this paper proposed and practiced a new simulation-based experimental teaching system deeply integrating Chinese educational wisdom. Taking "incremental progress and learning by guided inquiry" as the core philosophy, through a four-in-one paradigm transformation of "task modularization, scenario virtualization, technological frontier, and integration deepening", this paper promoted the teaching to shift from closed skill verification to open engineering innovation and constructed a complete implementation path including "closed-loop iterative teaching process" …

Four-Dimensional Gradient Integration: Reform And Practice On Modeling And Simulation Courses For Management Disciplines, Bin Wu, Huagang Tong, Zhiyong Cui, Feiyi Yan

Journal of System Simulation

In the process of course implementation, local universities generally face problems such as students' weak mathematical and physical foundations, disconnection between teaching content and technological frontiers, single teaching method, and fragmented cultivation of practical capability. Based on long-term teaching reform practice, a four-dimensional gradient integration teaching model of "value guidance, knowledge restructuring, scenario innovation, and capability progression" was proposed, and its theoretical logic, implementation path, and practical effect were systematically elaborated. This model can effectively stimulate students' intrinsic motivation for learning, promote the digital and intelligent updating of teaching content, expand the teaching scenario of industry-education integration, and realize …

Improved Nsga-Ii For Dual-Resource Flexible Job Shop Scheduling Considering Worker Load, Guohui Zhang, Yuan Ren, Changjun Wu, Xiaofei Kou

Journal of System Simulation

For the dual-resource-constrained flexible job shop scheduling problem considering worker load, an evolutionary algorithm integrating reinforcement learning was proposed. A three-stage encoding conforming to the problem characteristics was designed, and three initialization methods were combined to improve the population quality; a left-insertion decoding method based on worker load was designed to ensure that the completion time of the operation is less than the maximum processable time of the worker on the current day; two neighborhood structures based on the critical path were constructed to enhance the local exploration ability of the population; reinforcement learning was integrated to enable the …

Parameter Identification Of Permanent Magnet Synchronous Motors Based On Igwo-Aekf, Lei Yao, Zijian Zheng, Tianhao Li, Yulun Chi

Journal of System Simulation

The accuracy of the traditional EKF in parameter identification of the PMSM tends to be degraded under load changes or abrupt changes in internal parameters of the motor. This paper proposes an IGWO adaptive interconnected Kalman filter observer, which constructs an adaptive mechanism that combines the innovation and residuals to achieve dynamic adjustment of the process noise matrix and system noise matrix, thereby avoiding the problem of reduced parameter identification accuracy due to reliance on fixed covariance matrices under operating condition changes. A multi-parameter interconnected coupling compensation identification model for PMSM is built to mitigate the effects of measurement noise …

Research On Control Strategy For Shortest Time Occupancy Of Auv Based On Improved Td3, Wenzhe Ren, Min Li, Xiangguang Zeng, Tao Zhang, Dijie Xie, Bei Peng

Journal of System Simulation

Existing occupancy models fail to fully consider the interference of underwater time-varying ocean currents and task time constraints, and AUVs lacks real-time motion control. To address these issues, a shortest time occupancy method based on quantile regression and distributed TD3 was proposed. The Bayesian inference method was used to identify hydrodynamic parameters, and the kinematic and dynamic models of AUVs were established; the shortest time occupancy equation was constructed, and the occupancy target point and occupancy time were solved; a first-order Gauss-Markov process was introduced to simulate the time-varying ocean current environment, and the training of control strategy for AUV …

Impact Angle-Constrained Dive Maneuver Guidance Method For Hypersonic Vehicles, He Wang, Gang Lei, Shaopeng Li

Journal of System Simulation

To address the impact angle control and maneuvering flight problem of hypersonic vehicles in the dive phase, this paper proposed a tracking guidance method integrating optimal Bézier curves and super-twisting sliding mode control. A three-dimensional Bézier curve trajectory satisfying the impact angle constraint was designed, and the maneuvering flight in dive phase was achieved by adding dynamic control points; to optimize impact velocity, a rapid calculation method for the impact velocity of the vehicle flying along the curve was derived, and the optimal reference trajectory was obtained by optimizing the control point parameters through sequential quadratic programming; to ensure …

Application Of Improved Multi-Objective Differential Algorithm In Robotic Arm Multi-Objective Trajectory Planning, Manqiang Liu, Ziqiang Shang

Journal of System Simulation

It is difficult for single-objective trajectory planning methods to meet the requirements of precision, diversity and complexity of robotic arms. A trajectory planning model based on an improved multi-objective differential evolution algorithm (guided multi-objective differential evolution, GMODE) algorithm is proposed. Cubic polynomial interpolation and B-spline curves are employed to construct multi-objective functions, while GMODE is adopted to overcome the limitations of traditional algorithms, such as insufficient population diversity, the tendency to fall into local optima, and slow convergence. A grouping strategy, parameter generation mechanism, and elite mutation based on fuzzy Cmeans clustering are introduced to optimize B-spline control nodes. …

Robot Friction Force Compensation Algorithm Integrating Temperature And Speed Factors, Jinwang Lü, Ankai Ying, Ming Li, Tao Song, Jie Zhang, Fanghui Qiu, Changcheng Shi, Guokun Zuo, Jialin Xu

Journal of System Simulation

Insufficient friction force compensation accuracy degrades motion smoothness, stability, and assistive compliance of elbow joint rehabilitation robots. To address this issue, an improved Stribeck friction force model integrating temperature and speed factors was proposed. The model employed an exponentially decaying friction factor to describe the characteristic that the increase rate of friction force slowed down with the rise of the robot's operating speed and designed a viscous function considering temperature effects to suppress friction force fluctuations caused by temperature changes. Experimental results indicate that the model achieves stable friction force compensation under different operating states of the robot and has …

Current Status And Prospects Of Complex Scene Reconstruction Based On Gaussian Splatting, Hong'an Li, Jiale Yang, Qingfang Liu, Yu Shi

Journal of System Simulation

Three-dimensional Gaussian splatting (3DGS) provides an alternative approach for novel view synthesis from the perspective of explicit representation. By reconstructing scenes using 3D Gaussian primitives and replacing traditional ray integration with a point-based rasterization process, it not only improves training and rendering efficiency but also offers new insights for complex scene reconstruction. This paper divided 3DGS-based complex scene reconstruction methods into three major categories and elaborated on them around large-scale scenes, sparse views, and dynamic scenes. It reviewed the current development status of this field and pointed out possible future research directions.

Pattern Identification And Mechanism Analysis Of Nonlinear Oscillations In Grid-Connected Direct-Drive Wind Turbines, Libin Wen, Shaopu Tang, Xianfa Hu, Jinji Xi, Tongtong Zhang, Hong Hu, Weijie Zhang

Journal of System Simulation

Taking a grid-connected direct-drive wind turbine system as an example, a comprehensive model is developed that incorporates nonlinear elements such as prime mover control, machine-side and grid-side converter control, multiple limiters, and control switching. A nonlinear oscillation pattern identification method based on density clustering and manual identification is proposed. The results show that the proposed method can efficiently identify various typical patterns, including quasi-constant amplitude oscillations, period-doubling oscillations, and chaotic oscillations. Oscillations dominated by nonlinear factors such as control switching, limiter collision, and limiter saturation are essentially caused by the transition of the associated components from passive responses to …

Experimental And Simulation Study On Energy Release Of Extended Sources Influenced By Atmospheric Pressure Variation, Kai Guo, Feiyu Zhao, Hao Zhang, Liang Wang, Kai Zhang

Journal of System Simulation

To investigate the energy release characteristics of extended sources in low-pressure environments, a combined experiment and simulation approach was adopted. Four typical altitudecorresponding pressures were selected as experimental conditions. An infrared thermal imager was employed to monitor parameters such as combustion temperature, radiance, and combustion area during the combustion process of the extended source. When the pressure decreases from 101 kPa to 30 kPa, the ignition time of the extended source doubles; the total energy release attenuates by 44.78%, and the combustion area reduces by 45.95%, but the fluctuations of peak temperature and average temperature are less than 3%, …

Object Detection Networks And Their Interpretability In Rain, Fog, And Snow Scenarios, Yanji Jiang, Jiayu Cui, Hao Dong, Daqian Liu, Bowen Fei, Miao Yu, Jinshan Huang

Journal of System Simulation

To address the severe degradation of object detection performance under extreme weather conditions, a detection framework based on the Kolmogorov-Arnold theorem, termed KADet, is proposed. A dynamic Kolmogorov-Arnold Transformer is designed, which leverages learnable nonlinear activation functions to enhance the modeling capability for complex distortions introduced by weather degradation. A Kolmogorov-Arnold spatial-channel network is developed by integrating KAT convolution with spatial-channel convolution to strengthen feature learning of relationships between targets and backgrounds in degraded scenes. An improved loss function is introduced to guide the optimization of the activation functions, and interpretability is analyzed through visualization of their curves. …

Lafe0.925ti0.075o3 Perovskite Coated With Ethyl Cellulose: A High-Stability, Low-Hysteresis Platform For Advanced Humidity Sensing, Akhmad Futukhillah Fataba Alaih, Djoko Triyono, Rifqi Almusawi Rafsanjani

Makara Journal of Science

Humidity sensors are pivotal in numerous sectors, such as environmental monitoring, industrial automation, and health-care. This study presents the fabrication and detailed evaluation of an advanced humidity sensor using ethyl cellulose (EC)-coated LaFe_0.925Ti_0.075O₃ (LFTO) perovskite nanoparticles synthesized via the sol-gel method. The LFTO nanopar-ticles exhibited a mesoporous structure with an enhanced surface area, which significantly improved their moisture adsorption capabilities. The innovative application of EC coating addresses critical substrate adhesion issues, enhancing mechanical stability without compromising sensor sensitivity. Comprehensive performance evaluations revealed minimal hysteresis (

Efficient, Secure Learning And Resource Management For Wireless Networks, Jiahao Xue

USF Tampa Graduate Theses and Dissertations

The rapid advancement of wireless networks has enabled a wide range of applications such as federated learning (FL) and Internet of Things, with supporting technologies like radio frequency identification (RFID). However, resource management and security challenges in wireless networks remain insufficiently addressed for these representative applications. In this dissertation, we first investigate wireless resource management for FL, then study novel attack and defense mechanisms for RFID systems. Finally, we analyze the performance and security of artificial intelligence (AI)-enabled next generation wireless networks.

First, we investigate the client assignment and bandwidth allocation problem for FL in a multi-provider wireless setting. Unlike …

Building Trustworthy Information Systems: A Unified Framework For Comparative Risk Detection, Parisa Momeni

USF Tampa Graduate Theses and Dissertations

Risk detection in large scale information systems increasingly depends on heterogeneous data generatedby both centralized and distributed ecosystems. While centralized systems provide curated and validated reports, distributed environments produce large-scale and real-time observational evidence. Existing computational approaches analyze these ecosystems in isolation, limiting systematic comparison of risk repre-sentations across heterogeneous sources.

This dissertation presents a unified computational framework for comparative risk detection across centralized and distributed information systems. The framework provides a domain independent methodology for transforming heterogeneous risk reporting data into comparable multidimensional representations. To enable interpretable comparison of heterogeneous risk distributions, this work introduces the Geometric Overlap Score …

Efficient Performance Recovery Of Pruned Llms For End Devices, Paul Vardhan Bethapudi

Master’s Dissertations

Large Language Models have shown strong performance across reasoning, language understanding, and generation tasks, but their computational and memory requirements make deployment on low resource devices difficult. This dissertation studies efficient performance recovery of pruned LLMs, focusing on whether a pruned model can regain useful task performance through parameter-efficient fine-tuning while preserving the benefits of compression. The work uses Llama-3.2-3B-Instruct as the dense reference model and applies activationaware Wanda pruning at multiple sparsity levels(20%, 30% and 50%). The pruned models are evaluated on language modeling and reasoning tasks using WikiText-2 perplexity and GSM8K (Grade School Math 8000 problems) accuracy. Adaptation …

Exploring Gene Regulatory Neural Network Biocomputing Of Bacteria, Adrian Merle Ratwatte

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Artificial Intelligence (AI) has evolved from brain-inspired algorithms into a discipline that increasingly integrates with biological systems. While silicon-based platforms have advanced machine learning, they remain limited in energy efficiency and operation in environments beyond silicon. This motivates biological computing as an alternative, enabling efficient, resource-aware, and reconfigurable computing within living systems. This dissertation addresses these limitations by introducing a bacterial computing framework that models Gene Regulatory Networks (GRNs) as Gene Regulatory Neural Networks (GRNNs). The GRNN mirrors the structure and function of Artificial Neural Networks (ANNs) through gene-gene interactions across trans-omic layers, enabling natural, self-regulating information processing within living …

Transport And Interfacial Dynamics Of Electrochemical Formation Processes In Li-Based Batteries, Penghao Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Next-generation high-capacity batteries often rely on electrochemical formation processes rather than conventional intercalation alone. Such processes include lithium metal deposition and Li–O2 discharge-product formation, in which new phases form at the electrochemical interface during operation. As a result, the measured response reflects both electrochemical driving forces and the evolving product morphology. This coupling makes it difficult to interpret electrochemical data, microscopic observations, or model results in isolation. This dissertation examines this problem through three main studies and one preliminary extension on Li–O2 batteries and lithium metal deposition, each addressing a different factor that affects how electrochemical product formation is measured, …

Photoluminescent Studies Of Metal-Organic Frameworks For Small Molecule Sensing, Julia T. Dematteo

USF Tampa Graduate Theses and Dissertations

Metal–organic frameworks (MOFs) are highly tunable nanoporous materials with broad applications in gas separation, drug delivery, light harvesting, and chemical sensing. The incorporation of photoactive components into MOFs enables the development of optical sensors capable of detecting a wide range of analytes. Porphyrins and metalloporphyrins are particularly attractive for such applications due to their high molar absorptivity, strong emission quantum yields, and versatile interactions with chemical species.

In this work, two porphyrinic MOFs, PCN-222 and MOF1(Cd), are investigated. Both frameworks utilize tetrakis(4-carboxyphenyl)porphyrin (TCPP) as the organic linker, imparting optical functionality to the materials. Integration of TCPP into the framework induces …

Improved Method Of Evaluating Gas-Phase Air Cleaners Considering Adsorption And Desorption Mechanisms To And From Internal Test Chamber Surfaces, Casey Douglas Coffland

Dissertations and Theses

Portable air cleaners are playing an increasingly important role in managing indoor air quality in response to pressing issues like wildfire smoke events. Smoke is a complex mixture of gases and particulate matter; while established test methods exist for particulate matter, the dynamic behavior of gaseous pollutants is not well accounted for in current test methods. This is because prevailing air cleaner testing methods typically treat background losses as irreversible, whereas gases can engage in partitioning to and from test chamber surfaces. Ignoring these sorption interactions causes inaccurate estimation of an air cleaner's impact.

A new approach has been developed …

Metacognition As Disciplinary Infrastructure In Ai-Mediated Learning, Tina R. Austin, Jason Gulya, Nick Potkalitsky

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

Metacognition is often presented as a response to generative AI’s disruption of teaching and learning, yet the term has become too generalized to guide practice. In AI-mediated environments, asking students merely to “reflect on your thinking” is insufficient. Because AI tools can redistribute cognitive labor, their educational value depends on how students use them and whether that use supports disciplinary forms of reasoning. This essay argues that metacognition must be understood as disciplinary infrastructure: students cannot effectively monitor their thinking without understanding the epistemological and ontological demands of the field in which they are working. Classrooms therefore become sites where …

Law Or Flaw: A Double – Blind Study Comparing Student Comprehension Of Real And Ai Generated Legal Case Briefs, Grant Shostak, Nick Wintz, Melissa A. Petkovsek

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

In recent years, the U.S. legal system has seen an increase of legal filings using fictitious court cases or legal propositions generated by artificial intelligence (AI) (Stokel-Walker, 2026). Rules of professional responsibility require that lawyers review filings in which AI was used to ensure their accuracy (Missouri Bar, Office of Legal Ethics Counsel, 2024, Opinion No. 2024‑11) Despite this mandate, filings with fictitious cases and incorrect statements of law are being filed. Besides posing a threat to the parties to an action, such filings may set an unwarranted precedent for future cases. They also tie up court resources searching for …

Label-Flip Attack Detection Via Trust-Weighted Aggregation In Federated Learning For Underground Mine Security, Md Sazedur Rahman, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining operations are increasingly dependent on autonomous vehicles, robotic drilling systems, and intelligent inspection platforms operating in confined, GPS-denied tunnel environments. These systems rely on distributed perception models to interpret navigation cues, hazard warnings, and environmental signals in real time. While centralized deep learning can enhance model performance, transferring raw operational data across mining sites introduces serious confidentiality and security risks. Federated Learning (FL) offers a privacy-preserving alternative by enabling collaborative model training without sharing local datasets. However, deploying FL in underground mining introduces several critical challenges: (i) Training labels may be modified either maliciously by compromised clients or …