213,678 research outputs found
INVESTIGATION INTO MINE PILLAR DESIGN AND GLOBAL STABILITY USING THE GROUND REACTION CURVE CONCEPT
Pillars form an important support structure in any underground mine. A bulk of the overburden load is borne by the mine pillars. Thus, the strength of pillars has been a subject of detailed research over more than 6 decades. This work has led to the development of largely empirical pillar design formulations that have reduced the risk of pillar failures and mine collapse. Current research, however, has drawn attention to the fact that some of the assumptions used in the development of conventional pillar design methodologies are not always valid. Conventional pillar design methodology assumes that the pillars carry the dead weight of the overburden. This conventional method treats the pillars as passive structures. The limitation of this approach is that the self-supporting capacity of the overburden is not incorporated in pillar design. This suspension theory of pillar design treats the strata-pillar interaction problem as a classic case of static equilibrium, without detailing the interactions of the two structures.
Globally, multiple pillar design methods have been developed, based on this suspension theory. Each of these methods approaches the calculation of pillar stability a little differently with respect to material properties, excavation geometries and stress conditions. Most of these design methods are derived empirically and lack a mechanics-based approach. Moreover, there is a lack of a unified pillar design methodology that can be used to design all types of mine pillars using a mechanics-based approach.
The Ground Reaction Curve has been used as a means of correlating strata displacements to stress conditions. In addition, the Support Reaction Curve has been used in modeling the response of a support system under load, as a function of support properties and installation time with respect to opening development. In comparing the Ground Reaction Curves and Support Reaction Curves for different support systems, one can evaluate the effectiveness of installed support systems in maintaining the integrity of the excavated area(s).
This approach has been widely used in designing secondary (artificial) support systems in both civil tunneling and the mining industry. Encouraged by the successful use of this single method in designing secondary support systems, this research revisits this concept for mine pillar design. This research investigates the utilization of the Ground Reaction Curve and Support Reaction Curve for the design of mine pillar support systems with respect to anticipated pillar loading and opening convergence. In addition, a conceptual three-tier solution to the pillar design problem, using a proper combination of numerical, analytical and data-driven methods is suggested, and a flowchart for the pillar design methodology is proposed. At the focus of this proposed method lies the Ground Reaction Curve (GRC) Concept. This research effort tries to verify the proposed pillar design flowchart using in-mine instrumentation and numerical modeling.
For the purpose of this research, a deep longwall coalmine is instrumented to measure changes in pillar stress and associated roof convergence, due to mining activity. Subsequently, numerical models were developed in FLAC3D to model the geomechanical effects of underground longwall mining. The numerical modeling results are validated and calibrated using instrumentation data and a surface subsidence profile. The calibrated numerical models are further used to generate the Ground Reaction Curve for the overburden and Support Reaction Curve for the coal pillar. The comparison of both curves gives a detailed view of the overburden stability with respect to the mine pillar loading, in a more mechanics-based sense. The developed numerical approach can be used in future research and further development of this methodology for various mine types and different pillar support systems
Machine Learning Based Applications for Data Visualization, Modeling, Control, and Optimization for Chemical and Biological Systems
This dissertation report covers Yan Ma’s Ph.D. research with applicational studies of machine learning in manufacturing and biological systems. The research work mainly focuses on reaction modeling, optimization, and control using a deep learning-based approaches, and the work mainly concentrates on deep reinforcement learning (DRL). Yan Ma’s research also involves with data mining with bioinformatics. Large-scale data obtained in RNA-seq is analyzed using non-linear dimensionality reduction with Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), followed by clustering analysis using k-Means and Hierarchical Density-Based Spatial Clustering with Noise (HDBSCAN). This report focuses on 3 case studies with DRL optimization control including a polymerization reaction control with deep reinforcement learning, a bioreactor optimization, and a fed-batch reaction optimization from a reactor at Dow Inc.. In the first study, a data-driven controller based on DRL is developed for a fed-batch polymerization reaction with multiple continuous manipulative variables with continuous control. The second case study is the modeling and optimization of a bioreactor. In this study, a data-driven reaction model is developed using Artificial Neural Network (ANN) to simulate the growth curve and bio-product accumulation of cyanobacteria Plectonema. Then a DRL control agent that optimizes the daily nutrient input is applied to maximize the yield of valuable bio-product C-phycocyanin. C-phycocyanin yield is increased by 52.1% compared to a control group with the same total nutrient content in experimental validation. The third case study is employing the data-driven control scheme for optimization of a reactor from Dow Inc, where a DRL-based optimization framework is established for the optimization of the Multi-Input, Multi-Output (MIMO) reaction system with reaction surrogate modeling. Yan Ma’s research overall shows promising directions for employing the emerging technologies of data-driven methods and deep learning in the field of manufacturing and biological systems. It is demonstrated that DRL is an efficient algorithm in the study of three different reaction systems with both stochastic and deterministic policies. Also, the use of data-driven models in reaction simulation also shows promising results with the non-linear nature and fast computational speed of the neural network models
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The COVID-19 pandemic: resilient organisational response to a low-chance, high-impact event
The global healthcare sector is currently in the midst of the COVID-19 pandemic, a ‘low-chance, high-impact’ event which will require healthcare systems, and the organisations within them, to maintain organisational resilience in order to respond effectively. However, contrary to the instinctive reaction to tighten control, the quality of response depends on healthcare systems’ capacity to loosen control and, subsequently, enhance improvisation. Three factors critical to enhancing an organisation’s capacity for improvisation are highlighted; increasing autonomy, maintaining structure and creating a shared understanding. By drawing on the case of Christchurch Hospital’s response to a major earthquake, this paper demonstrates the vital role that improvisation can play within a clinical setting, when responding to a low-chance, high-impact event.
This article is made freely available for use in accordance with BMJ's website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained
Memory Switches in Chemical Reaction Space
Just as complex electronic circuits are built from simple Boolean gates, diverse biological functions, including signal transduction, differentiation, and stress response, frequently use biochemical switches as a functional module. A relatively small number of such switches have been described in the literature, and these exhibit considerable diversity in chemical topology. We asked if biochemical switches are indeed rare and if there are common chemical motifs and family relationships among such switches. We performed a systematic exploration of chemical reaction space by generating all possible stoichiometrically valid chemical configurations up to 3 molecules and 6 reactions and up to 4 molecules and 3 reactions. We used Monte Carlo sampling of parameter space for each such configuration to generate specific models and checked each model for switching properties. We found nearly 4,500 reaction topologies, or about 10% of our tested configurations, that demonstrate switching behavior. Commonly accepted topological features such as feedback were poor predictors of bistability, and we identified new reaction motifs that were likely to be found in switches. Furthermore, the discovered switches were related in that most of the larger configurations were derived from smaller ones by addition of one or more reactions. To explore even larger configurations, we developed two tools: the “bistabilizer,” which converts almost-bistable systems into bistable ones, and frequent motif mining, which helps rank untested configurations. Both of these tools increased the coverage of our library of bistable systems. Thus, our systematic exploration of chemical reaction space has produced a valuable resource for investigating the key signaling motif of bistability
An Expert System to Improve the Energy Efficiency of the Reaction Zone of a Petrochemical Plant
Energy is the most important cost factor in the petrochemical industry.
Thus, energy efficiency improvement is an important way to reduce these
costs and to increase predictable earnings, especially in times of high energy
price volatility. This work describes the development of an expert system for
the improvement of this efficiency of the reaction zone of a petrochemical
plant. This system has been developed after a data mining process of the variables
registered in the plant. Besides, a kernel of neural networks has been
embedded in the expert system. A graphical environment integrating the proposed
system was developed in order to test the system. With the application of
the expert system, the energy saving on the applied zone would have been about
20%.Junta de Andalucía TIC-570
Energy Efficiency and Economic Aspects of Mining Wastes Utilization within the Closed Cycle of Underground Gas Generator
Energy efficiency of coal gasification with possible utilization of mining wastes within
ecologically closed gas generator cycle has been considered. Technical and technological
performance of such gas generator and mechanism of material and heat balance on the basis of the
available analytical methods and practices as well as the developed author software have been
proposed. Heat carrier formed in the process of coal gasification has been used for the utilization.
Temperature of the utilization process within the industrially expedient limits being supported with
the help of either activation or attenuation of the gasification process. After specific treatment,
organogenic waste and domestic wastes are utilized by means of thermal decomposition within a
gas generator. Economic evaluation of the proposed means confirms the expediency of their
implementation in mines with industrial and balanced coal reserves as well as within the areas
where this energetic source has already been already mined out. Results of this investigation were
partially presented on international scientific and practical conference “Forum of Miners - 2017”.
They contain the researches, which were conducted within the project GP – 489, financed by
Ministry of Education and Science of Ukrain
Social Emotion Mining Techniques for Facebook Posts Reaction Prediction
As of February 2016 Facebook allows users to express their experienced
emotions about a post by using five so-called `reactions'. This research paper
proposes and evaluates alternative methods for predicting these reactions to
user posts on public pages of firms/companies (like supermarket chains). For
this purpose, we collected posts (and their reactions) from Facebook pages of
large supermarket chains and constructed a dataset which is available for other
researches. In order to predict the distribution of reactions of a new post,
neural network architectures (convolutional and recurrent neural networks) were
tested using pretrained word embeddings. Results of the neural networks were
improved by introducing a bootstrapping approach for sentiment and emotion
mining on the comments for each post. The final model (a combination of neural
network and a baseline emotion miner) is able to predict the reaction
distribution on Facebook posts with a mean squared error (or misclassification
rate) of 0.135.Comment: 10 pages, 13 figures and accepted at ICAART 2018. (Dataset:
https://github.com/jerryspan/FacebookR
Mechanosynthesis of nanocrystalline ZrB2-based powders by mechanically induced self-sustaining reaction method
Preparation of nanocrystalline ZrB2-based powder by aluminothermic and magnesiothermic reductions in M/ZrO2/B 2O3 (M = Al or Mg) systems was investigated. In this research, high energy ball milling was employed to persuade necessary conditions for the occurrence of a mechanically induced self-sustaining reaction (MSR). The course of MSR reactions were recorded by a noticeable pressure rise in the system during milling. Ignition times for ZrB2 formation by aluminothermic and magnesiothermic reductions were found to be 13 and 6 min, respectively. Zirconium diboride formation mechanism in both systems was explained through the analysis of the relevant sub-reactions. © 2013 Institute of Materials, Minerals and Mining Published by Maney on behalf of the Institute.Gobierno de España No. MAT2011- 2298
An Optimal Approach for Mining Rare Causal Associations to Detect ADR Signal Pairs
Abstract- Adverse Drug Reaction (ADR) is one of the most important issues in the assessment of drug safety. In fact, many adverse drug reactions are not discovered during limited premarketing clinical trials; instead, they are only observed after long term post-marketing surveillance of drug usage. In light of this, the detection of adverse drug reactions, as early as possible, is an important topic of research for the pharmaceutical industry. Recently, large numbers of adverse events and the development of data mining technology have motivated the development of statistical and data mining methods for the detection of ADRs. These stand-alone methods, with no integration into knowledge discovery systems, are tedious and inconvenient for users and the processes for exploration are time-consuming. This paper proposes an interactive system platform for the detection of ADRs. By integrating an ADR data warehouse and innovative data mining techniques, the proposed system not only supports OLAP style multidimensional analysis of ADRs, but also allows the interactive discovery of associations between drugs and symptoms, called a drug-ADR association rule, which can be further, developed using other factors of interest to the user, such as demographic information. The experiments indicate that interesting and valuable drug-ADR association rules can be efficiently mined. Index Terms- In this paper, we try to employ a knowledgebased approach to capture the degree of causality of an event pair within each sequence and we are going to match the data which was previously referred or suggested for treatment. � It is majorly used for Immediate Treatment for patients. However, mining the relationships between Drug and its Signal Reaction will be treated by In-Experienced Physician’
A text-mining system for extracting metabolic reactions from full-text articles
Background: Increasingly biological text mining research is focusing on the extraction of complex relationships
relevant to the construction and curation of biological networks and pathways. However, one important category of
pathway—metabolic pathways—has been largely neglected.
Here we present a relatively simple method for extracting metabolic reaction information from free text that scores
different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence
and location of stemmed keywords. This method extends an approach that has proved effective in the context of the
extraction of protein–protein interactions.
Results: When evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our
method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the
well-known protein-protein interaction extraction task.
Conclusions: We conclude that automated metabolic pathway construction is more tractable than has often been
assumed, and that (as in the case of protein–protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed
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