Bibliografías temáticas / Rock Machine (Gang)

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Literatura académica sobre el tema "Rock Machine (Gang)"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 28 de julio de 2025

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Artículos de revistas sobre el tema "Rock Machine (Gang)"

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Dormishi, Alireza, Mohammad Ataei, Reza Mikaeil, and Reza Khalo Kakaei. "Relations between Texture Coefficient and Energy Consumption of Gang Saws in Carbonate Rock Cutting Process." Civil Engineering Journal 4, no. 2 (2018): 413. http://dx.doi.org/10.28991/cej-0309101.

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Texture coefficient is one of the most influential parameters in rock engineering specifications in various projects including drilling, cutting, permeability of all-section drilling devices, etc. Meanwhile, investigating and forecasting the energy consumption of saw cutters are one of the most important factors in estimating the cutting costs. The present study aims to investigate the relationship between rock texture characteristics and the amount of energy consumption of the gang saw machine in the process of cutting carbonate rocks. To evaluate the effects of texture on the rocks' engineer
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Williams, Patrick, and Erik Hannerz. "Articulating the "Counter" in Subculture Studies." M/C Journal 17, no. 6 (2014). http://dx.doi.org/10.5204/mcj.912.

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Introduction As street protests and clashes between citizens and authorities in places as different as Ferguson, Missouri and Hong Kong in autumn 2014 demonstrate, everyday life in many parts of the world is characterised by conflicting and competing sets of cultural norms, values, and practices. The idea that groups create cultures that stand in contrast to “mainstream” or “dominant culture” is nothing new—sociology’s earliest scholars sought cultural explanations for social “dysfunctions” such as anomie and deviance. Yet our interest in this article is not about the problems that marginalise
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Brabazon, Tara, and Stephen Mallinder. "Off World Sounds: Building a Collaborative Soundscape." M/C Journal 9, no. 2 (2006). http://dx.doi.org/10.5204/mcj.2617.

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There are many ways to construct, shape and frame a history of popular music. From a focus on performers to a stress on cities, from theories of modernity to reveling in ‘the post,’ innovative music has been matched by evocative writing about it. One arc of analysis in popular music studies focuses on the record label. Much has been written about Sun, Motown, Factory and Apple, but there are many labels that have not reached this level of notoriety and fame but offer much to our contemporary understanding of music, identity and capitalism. The aim of this article is to capture an underwritten
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Libros sobre el tema "Rock Machine (Gang)"

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Clercq, Wil de, and Edward Winterhalder. Assimilation: Rock Machine Become Bandidos - Bikers United Against the Hells Angels. Indy Pub, 2021.

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Winterhalder, Edward, and Wil De Clercq. Assimilation: Rock Machine Become Bandidos - Bikers United Against the Hells Angels. ECW Press, 2010.

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Winterhalder, Edward, and Wil De Clercq. Assimilation: Rock Machine Become Bandidos--bikers United against the Hells Angels. ECW Press, 2010.

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Actas de conferencias sobre el tema "Rock Machine (Gang)"

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Zhang, Haoze, Bisheng Wu, Yuanxun Nie, Xi Zhang, and Zhaowei Chen. "Prediction of In-Situ Stresses by Using Machine Learning and Intelligent Optimization Algorithms." In 57th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2023. http://dx.doi.org/10.56952/arma-2023-0453.

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ABSTRACT In-situ stresses play an important role in affecting many geological processes such as hydraulic fracturing and CO2 storage, and a good understanding of the magnitude and direction of in-situ stresses is very important for deep energy exploitation. In this study, a machine learning model consisting of generative adversarial networks (GAN), particle swarm optimization (PSO) and support vector regression machine (SVRM) is proposed to predict the minimum in-situ horizontal principal stress (Shmin) from a series of existing experimental breakout data. First, the GAN and PSO are used to im
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Ma, Zhengchao, Maoya Hsu, Hao Hu, et al. "Hybrid Strategies for Interpretability of Rate of Penetration Prediction: Automated Machine Learning and SHAP Interpretation." In 58th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2024. http://dx.doi.org/10.56952/arma-2024-0315.

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ABSTRACT: Accurate prediction of rate of penetration (ROP) during petroleum drilling is crucial to optimize and guide field operations. However, due to the complex nonlinear relationship between drilling parameters and ROP, traditional empirical models often struggle to accurately predict ROP. This study introduces an automated machine learning (AutoML) for ROP prediction and utilizes SHAP (SHapley Additive exPlanations) to interpret the prediction results. The workflow framework based on this collaborative prediction strategy enables automated processing of data and automatic stacking ensembl
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Ma, Zhaoyang, Shuyu Sun, Bicheng Yan, Hyung Kwak, and Jun Gao. "Enhancing the Resolution of Micro-CT Images of Rock Samples via Unsupervised Machine Learning based on a Diffusion Model." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214883-ms.

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Abstract Objectives/Scope X-ray Micro-Computer Tomography (μ-CT) has been widely adopted in earth science and petroleum engineering due to its non-destructive characteristic. Meanwhile, this three-dimensional-imaging method can be integrated with computer simulation to investigate petrophysical properties of reservoir rocks at pore scales. However, the application of μ-CT is limited by the trade-off between field of view and resolution, and it is challenging to indicate the pore structure of rocks, especially for shale or carbonate rocks. To address this issue, deep-learning-based super-resolu
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Pan, W., J. Chen, S. Mohamed, H. Jo, J. E. Santos, and M. J. Pyrcz. "Efficient Subsurface Modeling with Sequential Patch Generative Adversarial Neural Networks." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/214985-ms.

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Abstract Subsurface modeling is important for subsurface resource development, energy storage, and CO2 sequestration. Many geostatistical and machine learning methods are developed to quantify the subsurface uncertainty by generating subsurface model realizations. Good subsurface models should reproduce depositional patterns in training images (satellite images, outcrops, digital rock, or conceptual models) that are important to fluid flow. However, current methods are computationally demanding, which makes it prohibitively expensive for building large-scale, detailed subsurface model realizat
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Zheng, Haiyan, and Botao Lin. "Intelligent Evaluation on Drillability of Shale Gas Formation in N2 Region of Changning Field in Sichuan, Southwest China." In International Geomechanics Symposium. ARMA, 2022. http://dx.doi.org/10.56952/igs-2022-181.

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Abstract Formation drillability evaluation plays an essential role in the exploitation of shale gas reservoirs. Currently, the common methods for evaluating formation drillability mainly include experimental methods, such as scratch tests and micro-drilling tests, and regression analysis. However, the applications of experimental methods and regression analysis cannot reasonably assess drillability due to a lack of well-core information and describe the strong heterogeneity of shale formations. In this study, a data-driven model based on the constrained K-means (CKM) method was proposed to eva
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