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"Mining engineering Simulation games."
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The unofficial guide to mining in Minecraft
\"It should come as no surprise that mining is a very important way to obtain resources in the game of Minecraft. It's also a very important way to get resources in the real world! This informative book will show young readers more about how people build real-life mines and what they mine for, including in-game resources such as diamonds, iron, gold, and coal. It will also show them how to use that information to make their Minecraft mines safer and even more productive!\"-- Provided by publisher.
Book
A Simulation Game in Mineral Exploration: A Mineral Adventure from Exploration to Exploitation
In recent decades, simulation has emerged as a pivotal educational tool, bolstering scientific knowledge and honing decision-making skills across diverse disciplines. Surgery and flight simulators are well-known tools used to practice and train safely in surgeries and piloting. Meanwhile, the development of simulation games advances in other scientific fields, such as economics, management, engineering, and mathematics. These simulations offer learners a risk-free virtual platform to apply and refine their knowledge, leveraging animations, graphics, and interactive environments to enrich the learning experience. In engineering, while simulation is widely utilized as a powerful training tool for heavy equipment and process handling, the creation of strategy games for educational purposes is less frequent. This gap primarily stems from the challenge of converting complex engineering concepts and theories into a user-friendly yet comprehensive setup that preserves the more difficult aspects. This study adopts a design-based research approach to develop and evaluate an educational simulation game aimed at enhancing probabilistic and spatial reasoning in mineral exploration. The application generates random scenarios, within which users deploy strategies based on their knowledge, while accommodating the randomness of physical phenomena. The simulation game is adopted as an educational tool in the course “Introduction to Mineral Exploration” in the School of Mining and Metallurgical Engineering of the National Technical University of Athens. Additionally, we present the outcomes of game analytics and a qualitative evaluation derived from three workshops at higher education institutions in Greece.
Journal Article
QoE‐driven multi‐UAV deployment scheme for emergency communication networks
When ground communication infrastructure within a region cannot be used for some reason, deploying unmanned aerial vehicle (UAV)‐mounted base stations is undoubtedly the most effective way to provide communication services. This paper investigates the problem of joint deployment and power allocation of multiple UAVs, where ground terminals (GTs) seek to maximize quality of experience (QoE). In the mixed line‐of‐sight and non‐line‐of‐sight environment, UAVs need to change position in order to collect channel information until deployment problem is solved. Moreover, only neighboring UAVs communicate with each other, which makes the problem more difficult to solve. In order to solve this problem, game theory is used to model this problem and design a distributed learning algorithm to maximize the QoE of all GTs in the entire system. Simulation results validate the effectiveness of the proposed learning algorithm in improving the QoE fairness and achieving the rapid deployment of UAVs. This paper investigates the problem of joint deployment and power allocation of multiple UAVs with the purpose of improving the quality of experience (QoE) of all ground terminals (GTs). In order to solve this problem, we use game theory to model this problem and design a distributed learning algorithm to maximize the QoE of all GTs in UAV‐assisted emergency communication networks.
Journal Article
A comparative study of methods for estimating model-agnostic Shapley value explanations
Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to explain predictions made by complex machine learning models in the industry and academia. There are several algorithmic approaches for computing different versions of Shapley value explanations. Here, we consider Shapley values incorporating feature dependencies, referred to as conditional Shapley values, for predictive models fitted to tabular data. Estimating precise conditional Shapley values is difficult as they require the estimation of non-trivial conditional expectations. In this article, we develop new methods, extend earlier proposed approaches, and systematize the new refined and existing methods into different method classes for comparison and evaluation. The method classes use either Monte Carlo integration or regression to model the conditional expectations. We conduct extensive simulation studies to evaluate how precisely the different method classes estimate the conditional expectations, and thereby the conditional Shapley values, for different setups. We also apply the methods to several real-world data experiments and provide recommendations for when to use the different method classes and approaches. Roughly speaking, we recommend using parametric methods when we can specify the data distribution almost correctly, as they generally produce the most accurate Shapley value explanations. When the distribution is unknown, both generative methods and regression models with a similar form as the underlying predictive model are good and stable options. Regression-based methods are often slow to train but quickly produce the Shapley value explanations once trained. The vice versa is true for Monte Carlo-based methods, making the different methods appropriate in different practical situations.
Journal Article
Stability Evaluation of the Goaf Based on Combination Weighting and Cloud Model
Goaf has become one of the most significant sources of hazard affecting the safety of metal and nonmetal mines. Evaluation of goaf stability is of paramount importance for mine safety production. First, 13 indices such as rock mass structure, geological structure, and goaf volume are selected based on engineering experience and literature review to assess the stability of goaf. These indices are classified according to the characteristics of each factor, and a stability evaluation system for underground mine goaf is constructed. Second, the analytic hierarchy process method based on group decision theory is utilized to calculate the subjective weight of each index. Additionally, the CRITIC method is used to calculate the objective weight of each index. Finally, game theory is used to combine the subjective and objective weights, thereby improving the accuracy of the index weight. The stability grade of the goaf is calculated using the normal cloud model. The FLAC3D numerical simulation is used to analyze the stability of the goaf and verify the accuracy of the model. The abovementioned model is utilized for assessing the stability of the goaf in the Duimenshan mine section. The results indicate that 90% of the goaf area is in a stable or relatively stable condition, while the remaining 10% is unstable. The evaluation outcomes were compared with FLAC3D numerical simulations, highlighting a scientific and reliable method with an accuracy rate of 90%.
Journal Article
Tripartite Evolutionary Game and Simulation Analysis of Coal Mining Safe Production Supervision under the Chinese Central Government’s Reward and Punishment Mechanism
In recent years, although coal mine accidents in China have decreased, they still occur frequently. Most previous studies on the evolutionary game of safety mining are limited to a focus on system dynamics and two-party game problems and lack a spatial graphic analysis of strategy evolution. The parameters adopted are too few, and the influencing factors considered are too simple. The purpose of the paper is to introduce more parameters to study which will have an important impact on the strategy choices of participants and the evolution path of the strategy over time. We construct a tripartite evolutionary game model of coal mining enterprises, local governments, and central governments. As our method, a payment matrix of participants and replicated dynamic equations is established, and we also implement parameter simulation in MATLAB. In summary, we found that the reward and punishment mechanism plays an important role in safe coal mining. Specifically, (1) intensifying rewards and penalties for coal mining enterprises and local governments will help encourage coal mining enterprises to implement safe production measures and local governments to implement central government safety supervision policies. However, increased rewards will reduce central government’s willingness to adopt incentive strategies. (2) The central government’s reward for coal mining enterprises’ safe production must be greater than the increased cost of safe production to encourage enterprises to implement such production. Economic incentives for local governments must be greater than the benefits of rent-seeking; only then will local governments choose to strictly implement supervision policies. (3) Increasing sales revenue and rent-seeking costs of coal mining enterprises can also encourage them to implement safe production. Therefore, a well-designed reward and punishment mechanism will change the behaviour of coal enterprises and improve the probability of safe production. The research presented in this paper further works on improving safe coal mining production and designing reasonable reward and punishment mechanisms.
Journal Article
Analysis of the Causative Mechanism of Subgrade Subsidence Based on Combined Weight
When a highway overlies a goaf, the cracking and subsidence of the highway subgrade seriously threaten the safe operation and maintenance of highways, including passenger safety. In this study, subgrade subsidence in the operation period of the G0611 Zhangye–Wenchuan Expressway from Biandukou to Menyuan was analyzed. First, the main factors influencing this kind of subsidence were analyzed using theoretical analysis, field investigation, and field detection. Then, an index system for these factors was constructed, composed of one target-layer, five criterion-layer, and seventeen indicator-layer indexes. The ANP and CRITIC methods were used to calculate the subjective and objective weights of each influencing factor index. The combined weights were obtained based on game theory, and the contribution degree of each index was determined. The primary and secondary relationships of the influencing factors of subgrade subsidence were inferred. The research results indicate that the foundation of the analyzed expressway section contains goaf areas, with poor filling performance, failure to fill in layers according to regulations, and poor drainage being the main reasons for subgrade subsidence. Based on the contribution degree of the indicator-layer influencing factors, high-energy-level dynamic compaction can be used to ram goafs so as to ensure the operational safety of the expressway.
Journal Article
Optimal dynamic pricing for public transportation considering consumer social learning
Effective public transportation pricing strategies are critical to reducing traffic congestion and meeting consumer demand for sustainable urban development. In this study, we construct a dynamic game pricing model and a social learning network model for consumers of three modes of public transportation including metro, bus, and pa-transit. In the model, the metro, bus, and pa-transit operators maximize their profits through dynamic pricing optimization, and consumers maximize their utility by adjusting their travel habits through social learning in the social network. The reinforcement learning algorithm is applied to simulate the model, and the results show that: (1) as consumers’ perceived sensitivity to different modes of travel increases, the market share and price of each mode of travel adjust accordingly. (2) When taking into account consumers’ social learning behavior, the market share of metros remains high, while the market shares of buses and pa-transit are relatively low. (3) As consumers become more sensitive to their perception of each travel mode, operators invest more resources in improving service quality to gain market share, which in turn affects the price of each travel mode. Our results provide decision support for optimal pricing of urban public transportation.
Journal Article
Toward a Machine Learning and Software Defined Network Approaches to Manage Miners’ Reputation in Blockchain
In blockchain, transactions between parties are regrouped into blocks, in order to be added to the blockchain’s distributed ledger. Miners are nodes of the network that generate new blocks according to the consensus protocol. The miner that adds a valid block to the distributed ledger is rewarded. However, to find a valid block, the miner needs to solve a computationally difficult problem, which makes it difficult to a single miner to gain rewards. Therefore, miners join mining pools, where the powers’ of miners are federated to ensure stable revenues. In public blockchains, access to mining pools is not restricted, which makes mining pools vulnerable to considerable threats such as: block withholding (BWH) attacks and distributed denial of service (DDoS) attacks. In the present work, we propose a new reputation based blockchain named PoolCoin based on a distributed trust model for a mining pools. The trust model used by PoolCoin is inspired from the job market signaling model. The proposed PoolChain blockchain allows pool managers the selection of trusted miners in their mining pools, while miners are able to evaluate them. Furthermore, to detect malicious miners that claim bigger computing capacity, we also provided a machine learning module to estimate the real miners’ capacities. The efficiency of the proposed trust model is studied and the obtained simulation results are presented and discussed. Thus, the model parameters’ are optimized in order to detect and exclude misbehaving miners, while honest miners are maintained in the mining pool.
Journal Article
Using Serious Game Techniques with Health Sciences and Biomedical Engineering Students: An Analysis Using Machine Learning Techniques
Project “Voice assistants and artificial intelligence in Moodle: a path to a smart university” -SmartLearnUni-. Project number: PID2020-117111RB-I00, funded by State Investigation Agency. Ministry of Science, Innovation and Universities. Government of Spain; Project “The use of serious virtual game scenarios as a resource for improving the teaching-learning process in teaching-learning process in Health Sciences and Engineering degrees”-Serious Games Virtual Space-. Project number: Nº 1-13-03-2023, funded by Vice-Rectorate for Teaching and Research Staff. University of Burgos (Spain).
Journal Article