Defining optimal drill-hole spacing: A novel integrated analysis from exploration to ore control

BackgroundIdentifying modules from time series biological data helps us understand biological functionalities of a group of proteins/genes interacting together and how responses of these proteins/genes dynamically change with respect to time. With rapid acquisition of time series biological data from different laboratories or databases, new challenges are posed for the identification task and powerful methods which are able to detect modules with integrative analysis are urgently called for. To accomplish such integrative analysis, we assemble multiple time series biological data into a higher-order form, e.g., a gene × condition × time tensor. It is interesting and useful to develop methods to identify modules from this tensor.ResultsIn this paper, we present MultiFacTV, a new method to find modules from higher-order time series biological data. This method employs a tensor factorization objective function where a time-related total variation regularization term is incorporated. According to factorization results, MultiFacTV extracts modules that are composed of some genes, conditions and time-points. We have performed MultiFacTV on synthetic datasets and the results have shown that MultiFacTV outperforms existing methods EDISA and Metafac. Moreover, we have applied MultiFacTV to Arabidopsis thaliana root(shoot) tissue dataset represented as a gene×condition×time tensor of size 2395 × 9 × 6(3454 × 8 × 6), to Yeast dataset and Homo sapiens dataset represented as tensors of sizes 4425 × 6 × 6 and 2920×14×9 respectively. The results have shown that MultiFacTV indeed identifies some interesting modules in these datasets, which have been validated and explained by Gene Ontology analysis with DAVID or other analysis.ConclusionExperimental results on both synthetic datasets and real datasets show that the proposed MultiFacTV is effective in identifying modules for higher-order time series biological data. It provides, compared to traditional non-integrative analysis methods, a more comprehensive and better view on biological process since modules composed of more than two types of biological variables could be identified and analyzed.

Managing uncertainty in integrated environmental modelling: The UncertWeb framework

The physical properties of the lithospheric and upper mantle's rock are determined by its composition and the in situ temperature and pressure conditions. Together, they have been referred to as the thermochemical structure. Information about the upper mantle's thermochemical structure could be obtained using methods from different disciplines of the earth sciences, in which the geophysical approaches show potential to map the 3D variations on both the regional and global scales. Thus, techniques for investigating the thermochemical structure in the spherical coordinates are needed, including forward modeling of the geophysical observables, calculating schemes of the thermophysical properties for the lithologies, and effective inversion algorithm, which is particularly important for large‐scale applications. This paper first demonstrates an adaptive meshing architecture based on the tetrahedral mesh by the sophisticated constructions in a spherical shell. Techniques that enable rapid calculations of the thermophysical properties of the upper mantle's rocks are introduced in length. Methodologies for constructing 3D thermochemical models and forward modeling geophysical observations, including an inversion sub‐routine that couples the lithostatic pressure and density variations to forward modeling, are introduced and examined in detail using synthetic data sets. We then introduce methods for determining 3D thermochemical structures of the upper mantle. The inverse problem is treated as a multi‐task evaluation process and solved using advanced stochastic optimizing techniques. Estimated uncertainties of the resultant thermochemical models are obtained simultaneously for error analysis. The proposed forward modeling and inversion techniques are validated using synthetic data sets in both forward and inversion circumstances. Limitations and further developments are discussed in the subsequent concluding remarks.

Analyzing factors and resource policymaking options for sustainable resource management and carbon neutrality in mining industry: Empirical study in China

An Individual Survival Distribution (ISD) models a patient's personalized survival probability at all future time points. Previously, ISD models have been shown to produce accurate and personalized survival estimates (for example, time to relapse or to death) in several clinical applications. However, off-the-shelf neural-network-based ISD models are usually opaque models due to their limited support for meaningful feature selection and uncertainty estimation, which hinders their wide clinical adoption. Here, we introduce a Bayesian-neural-network-based ISD (BNN-ISD) model that produces accurate survival estimates but also quantifies the uncertainty in model's parameter estimation, which can be used to (1) rank the importance of the input features to support feature selection and (2) compute credible intervals around ISDs for clinicians to assess the model's confidence in its prediction. Our BNN-ISD model utilized sparsity-inducing priors to learn a sparse set of weights to enable feature selection. We provide empirical evidence, on 2 synthetic and 3 real-world clinical datasets, that BNN-ISD system can effectively select meaningful features and compute trustworthy credible intervals of the survival distribution for each patient. We observed that our approach accurately recovers feature importance in the synthetic datasets and selects meaningful features for the real-world clinical data as well, while also achieving state-of-the-art survival prediction performance. We also show that these credible regions can aid in clinical decision-making by providing a gauge of the uncertainty of the estimated ISD curves.

A modified level-set method (MLSM) is proposed to simultaneously reconstruct the shape and electrical properties of 2-D objects. As a numerical technique, the formal level-set method (LSM) can retrieve the shape and position of objects, using synthetic/measurement data. In general, the constitutive parameters of an object (e.g., its relative complex permittivity) are among the a priori information needed for the LSM. For MLSM, an evolution strategy is proposed to simultaneously calculate both the shape and complex permittivity of a 2-D object. The initial guesses in respect of the complex permittivity and shape of the target object converge on their real values as the cost function is minimized. The cost function is regularized with two penalty terms. To prevent sudden change in the shape of the object, a curvature-based regularization is used. Also, Laplacian regularizer is used to reduce fluctuations in the object’s constitutive parameters during the process. Using different synthetic data sets, the capabilities of MLSM in microwave imaging and parameter estimation are evaluated. It is found that, using MLSM, it is possible to completely separate two adjacent objects, separated by a distance of one-fifteenth of a wavelength. The proposed method can retrieve targets of different relative permittivities, with less than 10% error. One interesting feature of this method is its high fidelity in retrieving the immersed one-tenth-wavelength targets in highly contrasting (up to as high as 8:1) domains. In the case of targets with little contrast (10%), the proposed method, with more iterations, can converge on a value, which is 57% more than the actual value. These features are valuable in distinguishing malignant tissue from normal tissue.

An implementation of differential evolution algorithm for inversion of geoelectrical data

Big data analytics has been transforming various industries and sectors including mining, metals, metallurgical, etc., and the mining sector, known for its complex and dynamic workforce requirements, is increasingly turning to Big Data Analytics to optimize its Human Resource Management (HRM) practices. The mining sector, being a significant economic segment, contributes to economic growth and is confronted with the dual challenges of a rapidly aging workforce and a shortage of skilled talent. As the mining industry is facing a talent crunch, HR has a significant role in attracting and retaining new talent in this industry, as well as ensuring that competent talent is brought in to meet the sector’s future demands Big data analytics for HR refers to the use of data analysis techniques to gain insights into employee behavior, engagement, productivity, and retention. The purpose of this paper is to explore the challenges and opportunities of big data analytics for HR, best practices for implementing it, and case studies of companies that have successfully implemented big data analytics for HR. The paper begins by outlining the importance of big data analytics for HR, highlighting its ability to provide HR professionals with a better understanding of employee behavior, engagement, and motivation. The challenges of big data analytics for HR are then discussed, including data quality and accessibility, integration of data from various sources, protection of data privacy, lack of expertise and resources, and resistance to change and adoption. The opportunities of big data analytics for HR are then presented, including predictive analytics for talent management, data-driven recruitment and selection, employee engagement and retention, performance management and productivity, and diversity, equity, and inclusion. Best practices for implementing big data analytics for HR are also discussed, including defining clear objectives and metrics, selecting the right tools and technologies, building a data-driven culture, collaborating with IT and other departments, and ensuring data privacy and security. Case studies of successful big data analytics in HR are presented to provide real-world examples of companies that have leveraged big data analytics to improve their HR functions. Examples include IBM's talent analytics program, Google’s use of big data analytics to identify key drivers of employee satisfaction and engagement, and leading mining company, referred to as “MinerCo,” leveraged Big Data Analytics to address HRM challenges and harness opportunities for improvement.

Generally, the determination of resource classification is only qualitative based on the geometric factors and geological complexity that control it. However, as the prospect area is found to have a reasonably heterogeneous sediment characteristic, a method is needed that can be used to increase the level of confidence in determining the Optimum Drill Hole Spacing. Therefore, this study uses the application of geostatistics with the global estimation variance (GEV) method based on the relative error value of each parameter, namely the thickness geometry and quality in the form of Ash and VM. This research was carried out in Jangga Aur village, Bathin XXIV District, Batang Hari Regency, Jambi Province, Working Area of PT Berkat Bara Persada Jobsite PT Inti Bara Nusalima.The Drill Hole Spacing Analysis (DHSA) results will obtain optimum spacing on resource classification based on relative error values, namely 0-10% for measured resources, 10-20% for indicated resources, and 20% for Inferred resources carried out the on-seam reference. Based on the results of the study, it was found that the seam reference used was Seam D, then the spacing distance of the drill hole on the coal seam of the research area, which had an average distance of 80 m, with geostatic analysis could be increased up to a distance of 250 m in measured resources, indicated resources of 450 m and Inferred 800 m.

In this study, the trend of unmanned aerial vehicle (UAV) technology and its applications in the mining industry were reviewed. To understand the trend of the UAV technology, the definition and classification of the UAVs, their major applications in each field, the characteristics of the sensors of the UAV were investigated. Some examples that used the UAV technology for planning, work management, facility management, safety management, and other works in the mining industry were reviewed. By introducing the UAV technology in the mining site, it is expected that the work efficiency related to mining operations would be increased. Therefore, the application of the UAV technology in the mining industry would be expanded further.

Textual data majorly reflects objective and subjective human specific knowledge. Focusing on big data in industrial and operation management, the value of textual data is oftentimes undermined. Optimal use of data reinforces the integrative modeling and analysis of RAM (Reliability, Availability, Maintainability). Data-driven reliability engineering and maintenance management, gains benefit from textual data, especially for identifying unknown failure modes and causes, and solving problems. The scientific challenge is how to effectively discover knowledge from text data and convert it into automated processes for inferential reasoning, predicting and prescribing. This paper outlines how the reliability-centered maintenance in production systems can be improved by explicating and discovering human-specific knowledge from maintenance reports and related textual documents. Hence, a theoretical model for text understanding is proposed, which is demonstrated as a proof-of-concept demonstrator using real world manufacturing datasets. The text understanding model is represented by a three-dimensional matrix comprising three indexes, i.e. text readability, word associations within texts as well as sentiment. The implementation of the model as a software prototype involves using text mining techniques and machine learning algorithms. This paper emphasizes on the importance of knowledge extraction from text in the context of industrial maintenance, by demonstrating how an increased value of text understandability of maintenance reports correlates to an early stage detection of failure, the reduction of human failures and leads to an immense improvement of explication of human knowledge.

Probabilistic Safety Analysis of the Nuclear Power Plants in Slovakia This paper gives the results of the safety analysis of the nuclear power plants in Slovakia. The probabilistic assessment of NPP concrete containment and technologically steel segment penetration for Probabilistic Safety Analysis (PSA) level 2 of VVER 440/213 in the case of seismic and LOCA accident is presented. There is showed summary of calculation models and calculation methods for the probability analysis of the structural integrity considering load, material and model uncertainties. The numerical simulations on the base of LHS method were realized in the system ANSYS and FReET. Copyright©2010 Preprint of KONBiN

Identification of mutations of the genes that give cancer a selective advantage is an important step towards research and clinical objectives. As such, there has been a growing interest in developing methods for identification of driver genes and their temporal order within a single patient (intra-tumor) as well as across a cohort of patients (inter-tumor). In this paper, we develop a probabilistic model for tumor progression, in which the driver genes are clustered into several ordered driver pathways. We develop an efficient inference algorithm that exhibits favorable scalability to the number of genes and samples compared to a previously introduced ILP-based method. Adopting a probabilistic approach also allows principled approaches to model selection and uncertainty quantification. Using a large set of experiments on synthetic datasets, we demonstrate our superior performance compared to the ILP-based method. We also analyze two biological datasets of colorectal and glioblastoma cancers. We emphasize that while the ILP-based method puts many seemingly passenger genes in the driver pathways, our algorithm keeps focused on truly driver genes and outputs more accurate models for cancer progression.

Extended Kalman filters (EKFs) that monitor innovations over time have been demonstrated to be effective at detecting slowly accumulating measurement faults [1]. This paper first demonstrates that a single cumulative monitor becomes increasingly sensitive to measurement error model uncertainty as the accumulation interval increases. As a result, the monitor’s false alarm and missed detection rates can differ significantly from predefined design parameters. In response, we investigate the use of a bank of cumulative innovations monitors for measurement fault detection in multisensor navigation systems. Focusing on a tightly coupled GPS+inertial EKF, high-fidelity Monte Carlo simulation that includes real-world model uncertainty demonstrates that the monitor bank maintains tighter compliance with specified false alert and missed detection requirements. The simulations also quantify the ability of multiple monitors to reduce time-to-detect. Practical considerations including implications for mitigation are discussed. Simulation results indicate significant improvement in detecting slowly accumulating measurement faults using the monitor bank compared to the single cumulative monitor for a runtime of 30 minutes. We describe a novel covariance analysis extension method and compare the predicted performance of our method to traditional snapshot innovations monitoring and the infinitely accumulating monitor in [1]. Covariance analysis and Monte Carlo simulation results are presented for a variety of fault detection profiles and inertial measurement unit (IMU) qualities (tactical-grade and aviation-grade). Data for the time-to-detect is presented alongside the position-domain bias induced by the fault at the time of detection; generally the monitor bank can detect the presence of faulty measurements after the position-domain bias has reached only tens of meters whereas the other methods may not detect until hundreds of meters.

The objective of this study is to assess the effectiveness of a number of control system synthesis methodologies as practical design tools. The effectiveness is measured by the extent to which a controller obtained by a specific methodology has to be tuned for it to work in a real implementation. For this purpose we consider as a test bed a highly flexible beam attached to a d.c. motor driven rigid hub. Included in the study are five synthesis methodologies. They are (i) classical PD control, (ii) the LQR technique, (iii) robust servomechanism theory, (iv) the Lyapunov method, and (v) Quantitative Feedback Theory (QFT). Used are extensive computer simulations and actual experiments with each of the controllers synthesized using these methodologies. Each of the compensators performed satisfactorily in simulations. In actual implementation, however, some of the controllers did not work as predicted by simulations. The higher the d.c. gain and the bandwidth of the synthesized controller it is less likely to work in reality. This was clearly observed with the LQR controller, the servo controller and the Lyapunov based controllers. The QFT controller is the only one that accounted for the bandwidth limit at the synthesis stage. The other controllers require much fine tuning by trial and error for satisfying actual physical limits including the bandwidth. It is established that controllers designed using methodologies which incorporate real practical considerations such as modeling uncertainties, bandwidth limitations and input saturations need very little tuning whereas the others yield controllers which after tuning are quite different from the synthesized ones.