An introduction to cut-off grade estimation [[electronic resource] /] / by Jean-Michel Rendu

• Record Nr.: UNINA9910454898503321
• Autore: Rendu J.-M
• Pubbl/distr/stampa: Littleton, Colo., : Society for Mining, Metallurgy, and Exploration, 2008
• ISBN: 1-61344-066-9; 0-87335-284-X
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (115 p.)
• Disciplina: 622/.7
• Soggetti: Ores - Grading; Ores - Sampling and estimation; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover; Title; Copyright; Contents; Preface; CHAPTER 1: Introduction; CHAPTER 2: General Principles; CHAPTER 3: Minimum Cut-off Grades; CHAPTER 4: Cut-off Grade for Polymetallic Deposits; CHAPTER 5: Cut-off Grade and Optimization of Processing Plant Operating Conditions; CHAPTER 6: Cut-off Grade and Mine Planning-Open Pit and Underground Selective Mining; CHAPTER 7: Cut-off Grade and Mine Planning- Block and Panel Caving; CHAPTER 8: Which Costs Should Be Included in Cut-off Grade Calculations?; CHAPTER 9: When Marginal Analysis No Longer Applies: A Gold Leaching Operation; CHAPTER 10: Mining Capacity and Cut-off Grade When Processing Capacity Is FixedCHAPTER 11: Processing Capacity and Cut-off Grade When Mining Capacity Is Fixed; CHAPTER 12: Mining and Processing Capacity and Cut-off Grade When Sales Volume Is Fixed; CHAPTER 13: Releasing Capacity Constraints: A Base Metal Example; CHAPTER 14: Relationship Between Mine Selectivity, Deposit Modeling, Ore Control, and Cut-off Grade; CHAPTER 15: Conclusions; Bibliography; Symbols; About the Author
• Sommario/riassunto: An Introduction to Cut-off Grade Estimation examines one of the most important calculations in the mining industry. Cut-off grades are essential to determining the economic feasibility and mine life of a ; project. Increased cut-off grades can reduce political risks by ensuring higher financial returns over a shorter period of time. Conversely, lower cut-off grades may increase project life with longer economic benefits to shareowners, employees, and local communities. Cut-off grades also impact reported reserves, which are closely monitored by stock exchanges and regulatory agencies. Author Dr.

Algorithms and Models for the Web Graph : 18th International Workshop, WAW 2023, Toronto, on, Canada, May 23-26, 2023, Proceedings / / Megan Dewar [and four others], editors

• Record Nr.: UNISA996534466403316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2023]; ©2023
• ISBN: 9783031322969; 9783031322952
• Edizione: [First edition.]
• Descrizione fisica: 1 online resource (203 pages)
• Collana: Lecture Notes in Computer Science Series ; ; Volume 13894
• Disciplina: 005.1
• Soggetti: Computer algorithms; Data mining; World Wide Web
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Correcting for Granularity Bias in Modularity-Based Community Detection Methods -- 1 Introduction -- 2 Hyperspherical Geometry -- 3 The Heuristic -- 4 Derivation of the Heuristic -- 5 Experiments -- 6 Discussion -- References -- The Emergence of a Giant Component in One-Dimensional Inhomogeneous Networks with Long-Range Effects -- 1 Introduction and Statement of Result -- 1.1 The Weight-Dependent Random Connection Model -- 1.2 Main Result -- 1.3 Examples -- 2 Proof of the Main Theorem -- 2.1 Some Construction and Notation -- 2.2 Connecting Far Apart Vertex Sets -- 2.3 Existence of a Giant ; Component -- 2.4 Absence of an Infinite Component -- References -- Unsupervised Framework for Evaluating Structural Node Embeddings of Graphs -- 1 Introduction -- 2 Framework -- 2.1 Input/Output -- 2.2 Formal Description of the Algorithm -- 2.3 Properties -- 3 Experimentation -- 3.1 Synthetic Graphs Design -- 3.2 Algorithmic Properties of the Framework -- 3.3 Role Classification Case Study -- 4 Conclusion -- References -- Modularity Based Community Detection in Hypergraphs -- 1 Introduction -- 2 Modularity Functions -- 3 Hypergraph Modularity Optimization Algorithm -- 3.1 Louvain Algorithm -- 3.2 Challenges with Adjusting the Algorithm to Hypergraphs -- 3.3 Our Approach to Hypergraph Modularity Optimization: h-Louvain -- 4 Results -- 4.1 Synthetic Hypergraph Model: h-ABCD -- 4.2 Exhaustive Search for the Best Strategy -- 4.3 Comparing Basic Policies for Different Modularity Functions -- 5 Conclusions -- References -- Establishing Herd Immunity is Hard Even in Simple Geometric Networks -- 1 Introduction -- 2 Preliminaries -- 3 Unanimous Thresholds -- 4 Constant Thresholds -- 5 Majority Thresholds -- 6 Conclusions -- References -- Multilayer Hypergraph Clustering Using the Aggregate Similarity Matrix.; 1 Introduction -- 2 Related Work -- 3 Algorithm and Main Results -- 4 Numerical Illustrations -- 5 Analysis of the Algorithm -- 5.1 SDP Analysis -- 5.2 Upper Bound on -- 5.3 Lower Bound on Dii -- 5.4 Assortativity -- 5.5 Proof of Theorem 1 -- 6 Conclusions -- References -- The Myth of the Robust-Yet-Fragile Nature of Scale-Free Networks: An Empirical Analysis -- 1 Introduction -- 2 Data -- 2.1 Network Collection -- 2.2 Network Categorization -- 2.3 Handling Weighted Networks -- 2.4 Preprocessing -- 3 Scale-Freeness Analysis -- 3.1 Scale-Freeness Classification Methods -- 3.2 Results -- 4 Robustness Analysis -- 4.1 Network Robustness -- 4.2 Configuration -- 4.3 Results -- 5 Conclusions -- 6 Appendix -- 6.1 Scale-Freeness Classification: Further Analysis -- 6.2 Robustness: Further Analysis -- 6.3 The Curious Case of Collins Yeast Interactome -- References -- A Random Graph Model for Clustering Graphs -- 1 Introduction -- 2 Preliminaries -- 3 Homomorphism Counts in the Chung-Lu Model -- 4 Random Clustering Graph Model -- 5 Homomorphism Counts -- 5.1 Extension Configurations -- 5.2 Expected Homomorphism Counts -- 5.3 Concentration of Subgraph Counts -- References -- Topological Analysis of Temporal Hypergraphs -- 1 Introduction -- 2 Method and Background -- 2.1 Temporal Hypergraphs -- 2.2 Sliding Windows for Hypergraph Snapshots -- 2.3 Associated ASC of a Hypergraph -- 2.4 Simplicial Homology -- 2.5 Zigzag Persistent Homology -- 3 Applications -- 3.1 Social Network Analysis -- 3.2 Cyber Data Analysis -- 4 Conclusion -- References -- PageRank Nibble on the Sparse Directed Stochastic Block Model -- 1 Introduction -- 2 Main Results -- 3 Proofs -- 4 Results from Simulations -- 5 Remarks and Conclusions -- References -- A Simple Model of Influence -- 1 Introduction -- 2 Analysis for Random Graphs G(n,m) -- 3 Proof of Lemma 1.; 4 The Effect of Stubborn Vertices -- 5 The Largest Fragment in G(n,m) -- References -- The Iterated Local Transitivity Model for Tournaments -- 1 Introduction -- 2 Small World Property -- 3 Motifs and Universality -- 4 Graph-Theoretic Properties of the Models -- 4.1 Hamiltonicity -- 4.2 Spectral Properties -- 4.3 Domination Numbers -- 5 Conclusion and Further Directions -- References -- Author Index.
• Sommario/riassunto: This book constitutes the proceedings of the 18th International Workshop on Algorithms and Models for the Web Graph, WAW 2023, held in Toronto, Canada, in May 23-26, 2023.The 12 Papers presented in this volume were carefully reviewed and selected from 21 submissions. The aim of the workshop was understanding of graphs ; that arise from the Web and various user activities on the Web, and stimulate the development of high-performance algorithms and applications that exploit these graphs.