Tail Behavior Research Articles

Research on leaching kinetics and reactivity evaluation of copper tailings as precursor for alkali-activated materials

Copper tailings are by-products of copper mining, and because of their rich content of SiO2 and Al2O3, it is considered as a possible raw material for the preparation of geopolymer composites. In this paper, the leaching kinetics and reactivity of reactive elements Si and Al in copper tailings under alkali hydrothermal environment are investigated, and the reasons for the difference in reactivity between copper tailings and granulated blast furnace slag (GBFS), fly ash (FA) are also introduced. The results show that the leached Si and Al in the precursor materials are involved in the formation of N/C-A-S-H. Similar to FA and GBFS, the leaching behavior of reactive elements in copper tailings is mainly controlled by the diffusion behavior through the surface product layer. Additionally, the activation energy of Al in the precursor materials is lower than that of Si, which makes the Al-O bond easier to break in alkaline environment, thus causing the leaching rate of Al in the raw materials to be higher than that of Si. Meanwhile, the activation energies of Si and Al in copper tailings are 69.29kJ/mol and 33.91kJ/mol, respectively, and higher elemental activation energy makes the efficiency of elemental dissolution lower, which is also the main reason for the lower reactivity of copper tailings. This study aims to elucidate the interrelationships between physicochemical properties, leaching behavior and reactivity of copper tailings.

Compositional properties and geotechnical behavior of mining tailings: a review

Compositional properties and geotechnical behavior of mining tailings: a review

Porosity and cement controlling the response of artificially cemented tailings under hydrostatic loading to high pressures

Cemented tailings find various applications in mining, such as open-pit and underground backfill, dam decommissioning and filtered tailings stacking. This research investigates the compression behaviour of iron ore tailings (IOT) mixed with distinct amounts of Portland cement and compacted in different conditions through isotropic compression, pulse velocity, and unconfined compression tests. The results show the adequacy of the porosity/cement index (η/Civ) in predicting elastic and plastic characteristics of compacted filtered IOT - Portland cement blends, an original correlation that has not been reported by previous work. This index is useful in selecting the cement content and target density for essential parameters required to design cemented IOT stacks. Besides, both cement addition and compaction have promoted the tailings isotropisation (conversion of an anisotropic system to an isotropic one). The evolution of the Post-Yield Compression Line (PYCL) with cementation is shown. Finally, it is demonstrated that distinct initial (after compaction) porosities of uncemented specimens reach a unique PYCL after isotropic pressures above 100 MPa, and cemented specimens do not reach a unique PYCL even at 120 MPa of isotropic pressures. The results underscore the requirement of rigorous compaction control in the field and offer a methodology for the dosage and technological control of artificially cemented tailings.

Multivariate simulation using a locally varying coregionalization model

Understanding the response of materials in downstream processes of mining operations relies heavily on proper multivariate spatial modeling of relevant properties of such materials. Ore recovery and the behavior of tailings and waste are examples where capturing the mineralogical composition is a key component: in the first case, to ensure reliable revenues, and in the second one, to avoid environmental risks involved in their disposal. However, multivariate spatial modeling can be difficult when variables exhibit intricate relationships, such as non-linear correlation, heteroscedastic behavior, or spatial trends. This work demonstrates that the complex multivariate behavior among variables can be reproduced by disaggregating the global non-linear behavior through the spatial domain and looking instead at the local correlations between Gaussianized variables. Local linear dependencies are first inferred from a local neighborhood and then interpolated through the domain using Riemannian geometry tools that allow us to handle correlation matrices and their spatial interpolation. By employing a non-stationary modification of the linear model of coregionalization, it is possible to independently simulate variables and then combine them as a linear mixture that locally varies according to the inferred correlation, reproducing the global multivariate behavior seen on input variables. A real case study is presented, showing the reproduction of the reference multivariate distributions, as well as direct and cross semi-variograms.

Stress Field and Crack Pattern Interpretation by Deep Learning in a 2D Solid

ABSTRACTA nonlinear variational auto‐encoder (NLVAE) is developed to reconstruct the plane strain stress field in a solid with embedded cracks subjected to uniaxial tension, uniaxial compression, and shear loading paths. Latent features are sampled from a skew‐normal distribution, which allows encoding marked variations of the features of the stress field across the load steps. The NLVAE is trained and tested based upon stress maps generated with the finite element method (FEM) with cohesive zone elements (CZEs). The NLVAE successfully captures stress concentrations that develop across the loading steps as a result of crack propagation, especially when enhanced disentanglement is emphasized during training. Some latent variables consistently emerge as significant across various microstructure descriptors and loading paths. Correlations observed between the evolution of fabric descriptors and that of their significant stress latent features indicate that the NLVAE can capture important microstructure transitions during the loading process. Crack connectivity, crack eccentricity, and the distribution of zones of highly connected opened cracks versus zones with no cracks are the fabric descriptors that best explain the sequences of latent features that are the most important for the reconstruction of the stress field. Notably, the distributional shape, tail behavior, and symmetry of microstructure descriptor distributions have more influence on the stress field than basic measures of central tendency and spread.

Dispersion on the Complete Graph

ABSTRACTWe consider a synchronous process of particles moving on the vertices of a graph , introduced by Cooper et al. Initially, particles are placed on a vertex of . At the beginning of each time step, for every vertex inhabited by at least two particles, each of these particles moves independently to a neighbor chosen uniformly at random. The process ends at the first step when no vertex is inhabited by more than one particle. Cooper et al. showed that when the underlying graph is the complete graph on vertices, then there is a phase transition when the number of particles . They showed that if for some fixed , then the process finishes in a logarithmic number of steps, while if , an exponential number of steps are required with high probability. Here we provide a thorough asymptotic analysis of the dispersion time around criticality, where , and describe the transition from logarithmic to exponential time. As a consequence of our results we establish, for example, that the dispersion time is in probability and in expectation in when , and provide qualitative bounds for its tail behavior.

Tail and memory behaviour of the cryptocurrency prices and stock market indices

Tail and memory behaviour of the cryptocurrency prices and stock market indices

On the Tail Behavior for Randomly Weighted Sums of Dependent Random Variables with its Applications to Risk Measures

On the Tail Behavior for Randomly Weighted Sums of Dependent Random Variables with its Applications to Risk Measures

Laboratory Investigation on the Transitional Behaviour of Tailings from a Gold Mine Site in Australia

Laboratory Investigation on the Transitional Behaviour of Tailings from a Gold Mine Site in Australia

Sensitivity of mass flux reduction and mass removal of perfluoroalkyl substances to groundwater flow and transport parameter variability and heterogeneity

Heterogeneity of soil hydraulic (e.g., hydraulic conductivity (KS), porosity (θS)) and chemical (e.g., solid-phase adsorption (Kd)) properties complicates contaminant transport by creating spatial variability in sources of contaminant leaching. There is a knowledge gap on the effect of the interplay between these properties on the retardation and transport of per- and polyfluoroalkyl substances (PFAS) with different properties including carbon–fluorine chain-length and functional groups even in water-saturated conditions. Breakthrough curves have been used to evaluate PFAS transport behavior through heterogeneous media, including arrival time, maximum concentration, and tailing behavior. Contaminant mass flux reduction and mass removal correlations are also compared using numerical modeling to characterize PFAS transport through different source zones within a two-domain, heterogeneous system with comparison to homogeneous scenarios under water-saturated conditions. With heterogeneous properties, model sensitivity to KS was the highest among the other parameters and was controlled by the KS ratio between the different soils. The PFAS models in the homogeneous and heterogeneous scenarios were both sensitive to θS, depending on PFAS chain length. However, long-chain PFAS were less sensitive to θS variability compared to short-chain PFAS due to their higher Kd. The homogeneous and heterogeneous scenarios were equally sensitive to Kd variability, which was dependent on PFAS chain length.

Generalized sinh-Gaussian random fields

The aim of this contribution is to propose new flexible spatial models based on a class of generalized sinh transformations. Our proposal greatly extends the Gaussian random field mainly in terms of asymmetry, heavy tail behavior, and bimodality. We demonstrate that we obtain a valid random field. We ensure that it is mean square continuous. We derive explicit expressions for the moments, the covariance function, the skewness, and the kurtosis of the random fields. In comparison with the available skew-Gaussian models, the proposed models could capture a greater amount of skewness and also accommodate clustered spatial outliers. The proposed models are computationally very tractable within the Bayesian framework here adopted. They are compared with the Gaussian and skew-Gaussian spatial models through simulation studies and an application to the spatial prediction of weekly rainfall near Darwin, Australia. The result suggests that the predictive performance in terms of different criteria under our method tends to be smaller than those obtained from the competing ones.

Effect of shear-induced consolidation on the behaviour of a loose silt tailings

Tailings within a tailings storage facility (TSF) are subjected to monotonic loading and consolidation following tailings depositions into the impoundment and perimeter embankment raises. The aim of the current study was to identify how monotonic loading followed by consolidation influences the behaviour of a loose silt tailings deposit. The study quantified the effect of magnitude and duration of an applied monotonic load on the evolving undrained shear strength using triaxial testing. It also showed that peak undrained shear strengths are reached under lower shear-induced excess pore pressures and axial strains than inferred from standard triaxial testing. While the study showed how tailings benefitted from gains in peak undrained shear strength, it also highlighted some potential negative implications for timely detection of precursors to failure of TSFs by means of conventional monitoring.

Tail Behavior and Almost Sure Growth Rate of Superpositions of Ornstein–Uhlenbeck-type Processes

Tail Behavior and Almost Sure Growth Rate of Superpositions of Ornstein–Uhlenbeck-type Processes

Heavy-Tail Phenomenon in Decentralized SGD

Recent theoretical studies have shown that heavy-tails can emerge in stochastic optimization due to ‘multiplicative noise’, even under surprisingly simple settings, such as linear regression with Gaussian data. While these studies have uncovered several interesting phenomena, they consider conventional stochastic optimization problems, which exclude decentralized settings that naturally arise in modern machine learning applications. In this paper, we study the emergence of heavy-tails in decentralized stochastic gradient descent (DE-SGD), and investigate the effect of decentralization on the tail behavior. We first show that, when the loss function at each computational node is twice continuously differentiable and strongly convex outside a compact region, the law of the DE-SGD iterates converges to a distribution with polynomially decaying (heavy) tails. To have a more explicit control on the tail exponent, we then consider the case where the loss at each node is a quadratic function, and show that the tail-index can be estimated as a function of the step-size, batch-size, and the topological properties of the network of the computational nodes. Then, we provide theoretical and empirical results showing that DE-SGD has heavier tails than centralized SGD. We also compare DE-SGD to disconnected SGD where nodes distribute the data but do not communicate. Our theory uncovers an interesting interplay between the tails and the network structure: we identify two regimes of parameters (stepsize and network size), where DE-SGD can have lighter or heavier tails than disconnected SGD depending on the regime. Finally, to support our theoretical results, we provide numerical experiments conducted on linear regression and neural networks that suggest the heavier tails is correlated with better generalization in the decentralized setting.

The Conditional Autoregressive F-Riesz Model for Realized Covariance Matrices

Abstract We introduce a new model for the dynamics of fat-tailed (realized) covariance-matrix-valued time-series using the F-Riesz distribution. The model allows for heterogeneous tail behavior across the coordinates of the covariance matrix via two vector-valued degrees of freedom parameters, thus generalizing the familiar Wishart and matrix-F distributions. We show that the filter implied by the new model is invertible and that a two-step targeted maximum likelihood estimator is consistent. Applying the new F-Riesz model to U.S. stocks, both tail heterogeneity and tail fatness turn out to be empirically relevant: they produce significant in-sample and out-of-sample likelihood increases, ex-post portfolio standard deviations that are in the global minimum variance model confidence set, and economic differences that are either in favor of the new model or competitive with a range of benchmark models.

Inference for bivariate extremes via a semi-parametric angular-radial model

AbstractThe modelling of multivariate extreme events is important in a wide variety of applications, including flood risk analysis, metocean engineering and financial modelling. A wide variety of statistical techniques have been proposed in the literature; however, many such methods are limited in the forms of dependence they can capture, or make strong parametric assumptions about data structures. In this article, we introduce a novel inference framework for bivariate extremes based on a semi-parametric angular-radial model. This model overcomes the limitations of many existing approaches and provides a unified paradigm for assessing joint tail behaviour. Alongside inferential tools, we also introduce techniques for assessing uncertainty and goodness of fit. Our proposed technique is tested on simulated data sets alongside observed metocean time series’, with results indicating generally good performance.

Probing Deuteration-Induced Phase Separation in Supported Lipid Monolayers using Hyperspectral TERS Imaging.

In this study, we investigate the impact of deuteration on the formation of phase-separated domains in supported lipid monolayers using hyperspectral Tip-Enhanced Raman Spectroscopy (TERS) imaging. The intricate organization of biological membranes plays a crucial role in cellular functions. Various factors that influence domain formation have been identified in previous studies such as lipid tail length and cholesterol concentration. Deuterium labeling of lipids has proven useful for probing cellular structures and dynamics, but its impact on lipid phase separation remains underexplored. By examining 1:1 mixed monolayers of dipalmitoylphosphatidylcholine (DPPC) and deuterated DPPC on Au(111) surfaces, we reveal partial segregation of domains rich in deuterated and nondeuterated lipids. This study addresses a gap in knowledge by examining the impact of deuteration on lipid tail behavior, offering new insights into how even subtle structural modifications can influence phase behavior. Furthermore, it demonstrates that TERS can be a powerful, nondestructive, and label-free nanoanalytical tool for analyzing lipid membranes and advance the field of membrane biophysics.

Time-varying aggregate tail risk and cross-section of stock returns: Indian evidence

This article examines whether the time-varying aggregate tail risk is priced in the cross-section of average returns in the Indian equity market. The results show that the tail risk beta sorted portfolio returns increase monotonically with an increase in their tail risk sensitivity. More importantly, the Indian stocks provide economically and statistically significant risk premium for the tail beta risk factor. The findings remain robust after controlling for the Fama–French and momentum risk factors, and other individual characteristics governing tail behaviour of stocks. The study also provides evidence that institutional ownership and firm size affect tail risk premium.

Impact of Sharia‐Based Screening on Equity Risk: International Evidence

ABSTRACTThis paper investigates the impact of mainstream Sharia‐based screening methodologies on equity risk. The study is based on the daily returns of 23 pairs of stock indices belonging to different index providers, namely Dow Jones, FTSE, and S&P. For risk measurement, the study uses five Value at Risk (VaR) methods. Moreover, the conditional VaR (CVaR) is used to analyze the tail behavior of return distributions. The results show that Sharia‐based screening is a source of significant differences in risk. There is strong evidence that Islamic stock indices (ISIs) tend to be less risky than their conventional peers. Also, the screening process allows for keeping extreme negative returns at a lower level relative to unscreened indices. The analysis during the Dotcom and Subprime crises shows that, globally, the results are time‐invariant. In sum, Islamic screening is tantamount to prudential rules that lead to low‐risk portfolios; it is, in its essence, a risk‐reduction strategy. Findings suggest that Sharia‐compliant stocks would be of great attractiveness to risk‐averse investors. They will find their claim in this asset class, as its riskiness is relatively low. For banks, a lower VaR of Islamic investing implies fewer capital requirements for market risk when holding portfolios that include Sharia‐compliant stocks. Fewer capital requirements will enhance the banks' ability to invest and make profits, which will, in turn, result in reducing the cost of capital.

High-temperature mechanical performance and damage behavior of molybdenum tailings modified fly ash-based geopolymers

Molybdenum tailings modified fly ash based geopolymers were an eco-friendly building material with excellent mechanical performance and fracture toughness. In this work, cubic compressive test and acoustic emission technique were used to evaluate the effect patterns of molybdenum tailings contents and thermal temperature conditions on the high-temperature mechanical performance and damage behaviors of molybdenum tailings modified fly ash-based geopolymers. The molybdenum tailings contents were 0 %, 10 %, 20 %, 30 % and 40 %, and the target temperatures were 300 °C, 600 °C and 900 °C, respectively. The results of compressive tests showed that the low contents of molybdenum tailings could limit the growth of macroscopic cracks during the loading process and improve the compressive strength of fly ash-based geopolymers when the heat treatment temperature did not exceed 600 °C. The acoustic emission results showed that 20 % molybdenum tailings contents could improve the structural stability and high-temperature mechanical performance of fly ash-based geopolymers, reduced the growth of microcracks in the specimen during the loading process, and delayed the formation and cracking of macrocracks, but did not change the specimen's compressive damage mode. The 40 % contents of molybdenum tailings changed the compressive damage mode of the fly ash-based geopolymers, delayed the cracking of the matrix structure under the peak loading, and significantly improved its resistance to plastic deformation.