scholarly journals Large-scale destress blasting for seismicity control in hard rock mines: A case study

2020 ◽  
Vol 30 (2) ◽  
pp. 141-149 ◽  
Author(s):  
Isaac Vennes ◽  
Hani Mitri ◽  
Damodara Reddy Chinnasane ◽  
Mike Yao
Keyword(s):  
Large Scale ◽  
Hard Rock ◽  
Destress Blasting ◽  
Hard Rock Mines

scholarly journals Large-scale deformation in underground hard-rock mines

2015 ◽  
Vol 115 (7) ◽  
pp. 645-652 ◽  
Author(s):  
E. Karampinos ◽  
J. Hadjigeorgiou ◽  
P. Turcotte ◽  
F. Mercier-Langevin
Keyword(s):  
Large Scale ◽  
Hard Rock ◽  
Hard Rock Mines

scholarly journals Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 765 ◽  
Author(s):  
Weizhang Liang ◽  
Suizhi Luo ◽  
Guoyan Zhao ◽  
Hao Wu
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Hard Rock ◽  
Gradient Boosting ◽  
Pillar Stability ◽  
Rock Pillar ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
Extreme Gradient Boosting ◽  
Hard Rock Mines

Predicting pillar stability is a vital task in hard rock mines as pillar instability can cause large-scale collapse hazards. However, it is challenging because the pillar stability is affected by many factors. With the accumulation of pillar stability cases, machine learning (ML) has shown great potential to predict pillar stability. This study aims to predict hard rock pillar stability using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) algorithms. First, 236 cases with five indicators were collected from seven hard rock mines. Afterwards, the hyperparameters of each model were tuned using a five-fold cross validation (CV) approach. Based on the optimal hyperparameters configuration, prediction models were constructed using training set (70% of the data). Finally, the test set (30% of the data) was adopted to evaluate the performance of each model. The precision, recall, and F1 indexes were utilized to analyze prediction results of each level, and the accuracy and their macro average values were used to assess the overall prediction performance. Based on the sensitivity analysis of indicators, the relative importance of each indicator was obtained. In addition, the safety factor approach and other ML algorithms were adopted as comparisons. The results showed that GBDT, XGBoost, and LightGBM algorithms achieved a better comprehensive performance, and their prediction accuracies were 0.8310, 0.8310, and 0.8169, respectively. The average pillar stress and ratio of pillar width to pillar height had the most important influences on prediction results. The proposed methodology can provide a reliable reference for pillar design and stability risk management.

A Family With Autosomal-Dominant Progressive Sensorineural Hearing Loss

1996 ◽  
Vol 5 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Chris Halpin ◽  
Barbara Herrmann ◽  
Margaret Whearty
Keyword(s):  
Speech Production ◽  
Hearing Aids ◽  
Role Models ◽  
Speech Intelligibility ◽  
Large Scale ◽  
Speech Language Pathology ◽  
The Family ◽  
Patient Will ◽  
Language Pathology

The family described in this article provides an unusual opportunity to relate findings from genetic, histological, electrophysiological, psychophysical, and rehabilitative investigation. Although the total number evaluated is large (49), the known, living affected population is smaller (14), and these are spread from age 20 to age 59. As a result, the findings described above are those of a large-scale case study. Clearly, more data will be available through longitudinal study of the individuals documented in the course of this investigation but, given the slow nature of the progression in this disease, such studies will be undertaken after an interval of several years. The general picture presented to the audiologist who must rehabilitate these cases is that of a progressive cochlear degeneration that affects only thresholds at first, and then rapidly diminishes speech intelligibility. The expected result is that, after normal language development, the patient may accept hearing aids well, encouraged by the support of the family. Performance and satisfaction with the hearing aids is good, until the onset of the speech intelligibility loss, at which time the patient will encounter serious difficulties and may reject hearing aids as unhelpful. As the histological and electrophysiological results indicate, however, the eighth nerve remains viable, especially in the younger affected members, and success with cochlear implantation may be expected. Audiologic counseling efforts are aided by the presence of role models and support from the other affected members of the family. Speech-language pathology services were not considered important by the members of this family since their speech production developed normally and has remained very good. Self-correction of speech was supported by hearing aids and cochlear implants (Case 5’s speech production was documented in Perkell, Lane, Svirsky, & Webster, 1992). These patients received genetic counseling and, due to the high penetrance of the disease, exhibited serious concerns regarding future generations and the hope of a cure.

The Boeing 787 Dreamliner--A case study in large-scale design integration

2008 ◽  
Author(s):  
D. L. McMullin ◽  
A. R. Jacobsen ◽  
D. C. Carvan ◽  
R. J. Gardner ◽  
J. A. Goegan ◽  
...  
Keyword(s):  
Large Scale ◽  
Design Integration ◽  
Scale Design

Disruptive innovation and operationalization in local and sustainable food systems: Examining the University of Toronto-Local Food Plus partnership

2019 ◽  
Vol 6 (1) ◽  
pp. 120-139
Author(s):  
Lori Stahlbrand
Keyword(s):  
Food Systems ◽  
Large Scale ◽  
Food System ◽  
Local Food ◽  
Disruptive Innovation ◽  
Government Support ◽  
Sustainable Food ◽  
University Of Toronto ◽  
The University

This paper traces the partnership between the University of Toronto and the non-profit Local Food Plus (LFP) to bring local sustainable food to its St. George campus. At its launch, the partnership represented the largest purchase of local sustainable food at a Canadian university, as well as LFP’s first foray into supporting institutional procurement of local sustainable food. LFP was founded in 2005 with a vision to foster sustainable local food economies. To this end, LFP developed a certification system and a marketing program that matched certified farmers and processors to buyers. LFP emphasized large-scale purchases by public institutions. Using information from in-depth semi-structured key informant interviews, this paper argues that the LFP project was a disruptive innovation that posed a challenge to many dimensions of the established food system. The LFP case study reveals structural obstacles to operationalizing a local and sustainable food system. These include a lack of mid-sized infrastructure serving local farmers, the domination of a rebate system of purchasing controlled by an oligopolistic foodservice sector, and embedded government support of export agriculture. This case study is an example of praxis, as the author was the founder of LFP, as well as an academic researcher and analyst.

Algorithm of combining chromatography mass spectrometry-untargeted profiling and multivariate analysis for identification of marker-substances in samples of complex composition

2020 ◽  
Vol 86 (7) ◽  
pp. 12-19
Author(s):  
I. V. Plyushchenko ◽  
D. G. Shakhmatov ◽  
I. A. Rodin
Keyword(s):  
Mass Spectrometry ◽  
Multivariate Analysis ◽  
Large Scale ◽  
Complex Composition ◽  
Unified Protocol ◽  
Chromatography Mass Spectrometry ◽  
Marker Substances ◽  
Selection Testing ◽  
Untargeted Profiling

A viral development of statistical data processing, computing capabilities, chromatography-mass spectrometry, and omics technologies (technologies based on the achievements of genomics, transcriptomics, proteomics, metabolomics) in recent decades has not led to formation of a unified protocol for untargeted profiling. Systematic errors reduce the reproducibility and reliability of the obtained results, and at the same time hinder consolidation and analysis of data gained in large-scale multi-day experiments. We propose an algorithm for conducting omics profiling to identify potential markers in the samples of complex composition and present the case study of urine samples obtained from different clinical groups of patients. Profiling was carried out by the method of liquid chromatography mass spectrometry. The markers were selected using methods of multivariate analysis including machine learning and feature selection. Testing of the approach was performed using an independent dataset by clustering and projection on principal components.

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