Abstract: There has been an increase in the application of different biomaterials to repair hard tissues. Within these biomaterials, calcium phosphate (CaP) bioceramics are suitable candidates, since they can be biocompatible, biodegradable, osteoinductive, and osteoconductive. Moreover, during sintering, bioceramic materials are prone to form micropores and undergo changes in their surface topographical features, which influence cellular physiology and bone ingrowth. In this study, five geometrical properties from the surface of CaP bioceramic particles and their micropores were analyzed by data mining techniques, driven by the research question: what are the geometrical properties of individual micropores in a CaP bioceramic, and how do they relate to each other? The analysis not only shows that it is feasible to determine the existence of micropore clusters, but also to quantify their geometrical properties. As a result, these CaP bioceramic particles present three groups of micropore clusters distinctive by their geometrical properties. Consequently, this new methodological clustering assessment can be applied to advance the knowledge about CaP bioceramics and their role in bone tissue engineering.

Abstract: In this work, we consider a risk-averse ultimate pit problem where the grade of the mineral is uncertain. We derive conditions under which we can generate a set of nested pits by varying the risk level instead of using revenue factors. We propose two properties that we believe are desirable for the problem: risk nestedness, which means the pits generated for different risk aversion levels should be contained in one another, and additive consistency, which states that preferences in terms of order of extraction should not change if independent sectors of the mine are added as precedences. We show that only an entropic risk measure satisfies these properties and propose a two-stage stochastic programming formulation of the problem, including an efficient approximation scheme to solve it. We illustrate our approach in a small self-constructed example, and apply our approximation scheme to a real-world section of the Andina mine, in Chile.

Abstract: In 2017, the Minamata Convention (MC) on mercury (Hg) control entered into force. However, whether the MC is effective and how it reshapes the global Hg flow remain unclear. In this study, we established a method to detect inconsistencies in data on global Hg trade, and calculated the gap between the demand and supply of Hg to the artisanal and small-scale gold mining (ASGM) sector (i.e., the largest source of Hg emissions globally) in 39 countries across four regions. According to our results, inconsistencies in statistical data concerning Hg for ASGM activities exist in both Africa and Central and South America. Asia showed a considerably lower amount of Hg applied to ASGM than apparent Hg consumption; nevertheless, the largest consumer of Hg was Asia, predomi-nantly China and India. Many countries in which ASGM is conducted are already MC parties; however, only few submitted their national action plans (NAPs) or have established/enforced specific laws to curb Hg use in ASGM. Analysis of Hg-related trade information suggests that in 2017, the trade of metallic Hg disappeared in some African and Central and South American countries, but new trade flows of goods with higher Hg content emerged. The method established in this study can support the search for countries implementing ASGM with hidden Hg use and flows, thereby contributing to the planning of further Hg control regulations. To enforce sound Hg management, the submission of NAPs should also be promoted in addition to the expansion of MC parties.

Abstract: The large hadron collider (LHC) demands a huge amount of computing resources to deal with petabytes of data generated from high energy physics (HEP) experiments and user logs, which report user activity within the supporting worldwide LHC computing grid (WLCG). An outburst of data and information is expected due to the scheduled LHC upgrad, that is, the workload of the WLCG should increase by 10 times in the near future. Autonomous system maintenance by means of log mining and machine learning algorithms is of utmost importance to keep the computing grid functional. The aim is to detect software faults, bugs, threats, and infrastructural problems. This paper describes a general-purpose solution to anomaly detection in computer grids using unstructured, textual, and unsupervised data. The solution consists in recognizing periods of anomalous activity based on content and information extracted from user log events. This study has particularly compared one-class SVM, isolation forest (IF), and local outlier factor (LOF). IF provides the best fault detection accuracy, 69.5%.

Abstract: Assembling spatial units into meaningful clusters is a challenging task, as it must cope with a consequential computational complexity while controlling for the modifiable areal unit problem (MAUP), spatial autocorrelation and attribute multicolinearity. Nevertheless, these effects can reveal significant interactions among diverse spatial phenomena, such as segregation and economic specialization. Various regionalization methods have been developed in order to address these questions, but key fundamental properties of the aggregation of spatial entities are still poorly understood. In particular, due to the lack of an objective stopping rule, the question of determining an optimal number of clusters is yet unresolved. Therefore, we develop a clustering algorithm which is sensitive to scalar variations of multivariate spatial correlations, recalculating PCA scores at several aggregation steps in order to account for differences in the span of autocorrelation effects for diverse variables. With these settings, the scalar evolution of correlation, compactness and isolation measures is compared between empirical and 120 random datasets, using two dissimilarity measures. Remarkably, adjusting several indicators with real and simulated data allows for a clear definition of a stopping rule for spatial hierarchical clustering. Indeed, increasing correlations with scale in random datasets are spurious MAUP effects, so they can be discounted from real data results in order to identify an optimal clustering level, as defined by the maximum of authentic spatial self-organization. This allows singling out the most socially distressed areas in Greater Santiago, thus providing relevant socio-spatial insights from their cartographic and statistical analysis. In sum, we develop a useful methodology to improve the fundamental comprehension of spatial interdependence and multiscalar self-organizing phenomena, while linking these questions to relevant real world issues. (c) 2015 Elsevier Ltd. All rights reserved.

Abstract: Web directories allow Web users to browse a hierarchy of categories, under which different types of resources are classified. We study the problem of maintaining a Web directory, that is, the problem of continually discovering and ranking resources that are relevant to the categories of the directory. We propose an unsupervised computational method that conducts the maintenance of the directory by analyses of user browsing data. The method is based on the extraction and classification of user sessions (sequences of resources selected by users) into the categories of the directory. In addition, we show that the directory maintenance method can be slightly modified to find queries that are useful to find relevant resources allowing users to switch from directory browsing to query formulation. Experimental results allow for affirmation that the proposed methods are effective, that they attain identification of new pages in each category and also recommend related queries with high precision, without; needing labeled data to conduct traditional web page and query classification tasks.

Abstract: In underground mining, the design of rock pillars is of crucial importance as these are structures that allow safe mining by maintaining the stability of the surrounding excavations. Pillar design is often a complex task, as it involves estimating the loads at depths and the strength of the rock mass fabric, which depend on the intact strength of the rock and the shape of the pillar in terms of the aspect ratio (width/height). The design also depends on the number, persistence, orientation, and strength of the discontinuities with respect to the orientation and magnitude of the stresses present. Solutions to this engineering problem are based on one or more of the following approaches: empirical design methods, practical experience, and/or numerical modeling. Based on the similarities between masonry structures and rock mass characteristics, an equivalent approach is proposed as the one commonly used in masonry but applied to rock pillar design. Numerical models using different geometric configurations and state of stresses are carried out using a finite difference numerical approach with an adapted masonry model applied to rocks. The results show the capability of the numerical approach to replicate common types of pillar failure modes and stability thresholds as those observed in practice.

Abstract: The energy demand in Chile arises mostly from mining, its largest industry that accounts for about 35% of total electricity consumption. Energy generation to satisfy this demand depends completely on imported fossil fuels. As a result, the mining industry faces several energy related challenges. In particular, the cost and environmental impact of fuel sources are threatening the competitiveness of the industry and urge for new developments. In that regard, the importance of using clean and cost-competitive renewable energy sources has increased significantly in Chile and several government policies helped to increase the investment in them. The impact has been particularly large in the development of solar energy in the northern part of the country, where almost all mines are located. In fact, the country has become one of the largest solar markets in Latin America thanks to its abundant solar resources, favorable market conditions, and successful policy reforms. Solar energy then, could play a significant role as an alternative to satisfy the mining industry's energy demand offering a broad range of technological solutions. This study examines the key issues – barriers and drivers-influencing the adoption of solar technologies in the Chilean mining industry from the perspective of mining actors. As a result of the analysis the paper also provides a scope for appropriate policy interventions.

Abstract: The paper investigates new opportunities for innovation and linkages associated to mining activities in Brazil, Chile and Peru. Three types of opportunities were researched: demand side, supply side and local specificities. The last source of opportunities is key for natural resource related activities. The evidence shows that an increasing demand is introducing important incentives for innovation and local suppliers. Nevertheless, a hierarchical value chain, dominated by few large firms, and poor linkages is blocking the diffusion of innovations and hindering suppliers' development. The emergence of a group of highly innovative suppliers, which were identified in the three countries, is explained mostly by new technological and knowledge opportunities, which are not exploited by large incumbents and open spaces for new entrants. Local specificities are also key in the explanation of local suppliers. It remains a challenge however, how these, most of which were created to satisfy local needs, will move from local to global.

Abstract: Open pit mine production scheduling (OPMPS) is a decision problem which seeks to maximize net present value (NPV) by determining the extraction time of each block of ore and/or waste in a deposit and the destination to which this block is sent, e.g., a processing plant or waste dump. Spatial precedence constraints are imposed, as are resource capacities. Stockpiles can be used to maintain low-grade ore for future processing, to store extracted material until processing capacity is available, and/or to blend material based on single or multiple block characteristics (i.e., metal grade and/or contaminant). We adapt an existing integer-linear program to an operational polymetallic (gold and copper) open pit mine, in which the stockpile is used to blend materials based on multiple block characteristics, and call it ((P) over cap (la)). We observe that the linear programming relaxation of our objective function is unimodal for different grade combinations (metals and contaminants) in the stockpile, which allows us to search systematically for an optimal grade combination while exploiting the linear structure of our optimization model. We compare the schedule of ((P) over cap (la)) with that produced by (P-ns) which does not consider stockpiling, and with ((P) over tilde (la)), which controls only the metal content in the stockpile and ignores the contaminant level at the mill and in the stockpile. Our proposed solution technique provides schedules for large instances in a few seconds up to a few minutes with significantly different stockpiling and material flow strategies depending on the model. We show that our model improves the NPV of the project while satisfying operational constraints. (C) 2019 Elsevier Ltd. All rights reserved.