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Rajive Ganguli
Department of Mining Engineering, University of Utah, Salt Lake City, UT 84109, USA

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Journal article
Published: 04 August 2021 in COVID
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The lockdown policies enacted in the spring of 2020, in response to the growing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, have remained a contentious policy tool due to the variability of outcomes they produced for some populations. While ongoing research has illustrated the unequal impact of Coronavirus disease (COVID-19) on minority populations, research in this area has been unable to fully explain the mechanisms that produce these findings. To understand why some groups have been at greater risk of contracting COVID-19, we employ structural inequality theory to better understand how inequality may impact disease transmission in a pandemic. We used a novel approach that enabled us to focus on the microprocesses of structural inequality at the zip code level to study the impact of stay-at-home pandemic policies on COVID-19 positive case rates in an urban setting across three periods of policy implementation. We then analyzed data on traffic volume, income, race, occupation, and instances of COVID-19 positive cases for each zip code in Salt Lake County, Utah (USA) between 17 February 2020 and 12 June 2020. We found that higher income, percent white, and white-collar zip codes had a greater response to the local stay-at-home order and reduced vehicular traffic by nearly 50% during lockdown. The least affluent zip codes only showed a 15% traffic decrease and had COVID-19 rates nearly 10 times higher. At this level of granularity, income and occupation were both associated with COVID-19 outcomes across all three stages of policy implementation, while race was only predictive of outcomes after the lockdown period. Our findings illuminate underlying mechanisms of structural inequality that may have facilitated unequal COVID-19 incidence rates. This study illustrates the need for more granular analyses in policy research and adds to the literature on how structural factors such as income, race, and occupation contribute to disease transmission in a pandemic.

ACS Style

Daniel Mendoza; Tabitha Benney; Rajive Ganguli; Rambabu Pothina; Cheryl Pirozzi; Cameron Quackenbush; Samuel Baty; Erik Crosman; Yue Zhang. The Role of Structural Inequality on COVID-19 Incidence Rates at the Neighborhood Scale in Urban Areas. COVID 2021, 1, 186 -202.

AMA Style

Daniel Mendoza, Tabitha Benney, Rajive Ganguli, Rambabu Pothina, Cheryl Pirozzi, Cameron Quackenbush, Samuel Baty, Erik Crosman, Yue Zhang. The Role of Structural Inequality on COVID-19 Incidence Rates at the Neighborhood Scale in Urban Areas. COVID. 2021; 1 (1):186-202.

Chicago/Turabian Style

Daniel Mendoza; Tabitha Benney; Rajive Ganguli; Rambabu Pothina; Cheryl Pirozzi; Cameron Quackenbush; Samuel Baty; Erik Crosman; Yue Zhang. 2021. "The Role of Structural Inequality on COVID-19 Incidence Rates at the Neighborhood Scale in Urban Areas." COVID 1, no. 1: 186-202.

Journal article
Published: 17 July 2021 in Minerals
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To achieve the goal of preventing serious injuries and fatalities, it is important for a mine site to analyze site specific mine safety data. The advances in natural language processing (NLP) create an opportunity to develop machine learning (ML) tools to automate analysis of mine health and safety management systems (HSMS) data without requiring experts at every mine site. As a demonstration, nine random forest (RF) models were developed to classify narratives from the Mine Safety and Health Administration (MSHA) database into nine accident types. MSHA accident categories are quite descriptive and are, thus, a proxy for high level understanding of the incidents. A single model developed to classify narratives into a single category was more effective than a single model that classified narratives into different categories. The developed models were then applied to narratives taken from a mine HSMS (non-MSHA), to classify them into MSHA accident categories. About two thirds of the non-MSHA narratives were automatically classified by the RF models. The automatically classified narratives were then evaluated manually. The evaluation showed an accuracy of 96% for automated classifications. The near perfect classification of non-MSHA narratives by MSHA based machine learning models demonstrates that NLP can be a powerful tool to analyze HSMS data.

ACS Style

Rajive Ganguli; Preston Miller; Rambabu Pothina. Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine. Minerals 2021, 11, 776 .

AMA Style

Rajive Ganguli, Preston Miller, Rambabu Pothina. Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine. Minerals. 2021; 11 (7):776.

Chicago/Turabian Style

Rajive Ganguli; Preston Miller; Rambabu Pothina. 2021. "Effectiveness of Natural Language Processing Based Machine Learning in Analyzing Incident Narratives at a Mine." Minerals 11, no. 7: 776.

Chapter
Published: 05 May 2020 in Hindu Kush-Himalaya Watersheds Downhill: Landscape Ecology and Conservation Perspectives
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Mongolia is a land locked country, with two powerful neighbors, Russia and China. Mongolia still has adequate water resources for its population. Waters from the Khangai and Khentii mountains are the source for a majority of the water runoff from the country. Despite sufficient water, a generic population shift from rural to urban locations is stressing water resources in some parts of the country. Ulaanbaatar, now home to almost half the country and located at the Tuul river, is expected to run out of water in the next few years. Like other parts of the country, it also suffers due to a lack of adequate wastewater treatment facilities, contamination from a large number of livestock and legacy industries. There are also concerns about water in the South Gobi region, that has been the hub of large scale mining. Almost 40% of the runoff ends in the Gobi desert. Mining, a major part of the economy in Mongolia, and not limited to the Gobi, impacts water resources. While newer mines use modern technologies to conserve water and minimize environmental impact, older mines and artisanal mines significantly impact local water resources.

ACS Style

Rajive Ganguli. Water in Mongolia: Sources, Uses and Issues, with Special Emphasis on Mining. Hindu Kush-Himalaya Watersheds Downhill: Landscape Ecology and Conservation Perspectives 2020, 813 -822.

AMA Style

Rajive Ganguli. Water in Mongolia: Sources, Uses and Issues, with Special Emphasis on Mining. Hindu Kush-Himalaya Watersheds Downhill: Landscape Ecology and Conservation Perspectives. 2020; ():813-822.

Chicago/Turabian Style

Rajive Ganguli. 2020. "Water in Mongolia: Sources, Uses and Issues, with Special Emphasis on Mining." Hindu Kush-Himalaya Watersheds Downhill: Landscape Ecology and Conservation Perspectives , no. : 813-822.

Article
Published: 06 February 2020 in Mining, Metallurgy & Exploration
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Sensors are everywhere in the mining industry, with sensor information being used to monitor and operate processes. Therefore, when sensor information is wrong, economic losses can occur. Unfortunately, sensor errors are usually not detected till they become large. This is problematic as most calibrate their sensors no more than once a year (Beamex, n.d.). Using principles of data mining, where all relevant information is tapped to glean hidden information, Pothina [8] designed an algorithm to detect errors in temperature sensors in gold stripping circuit in Pogo mine, Alaska. This paper continued his work by analyzing the behavior of the algorithm on baseline data and testing the algorithm in new data and under more rigorous conditions. It also made a change to the algorithm. The modified algorithm performed very well in the new data. It also worked well under the new error conditions. Three types of errors were seeded, a fixed additive error (+ 2%), a fixed subtractive error (− 2%), and a noisy, normally distributed error, with a mean value of + 2%. Artificially seeded errors were detected within about 20 gold stripping cycles. Inherent bias in operation impacted algorithm performance by increasing the number of cycles needed to detect errors. This paper reinforced Pothina’s [8] conclusion that when data mining approach is used, sensor errors can be detected even when they are pretty low. Significant economic losses can thus be minimized.

ACS Style

Eduardo Pimenta De Melo; Rajive Ganguli; Rambabu Pothina. Modification and Enhanced Testing of Data Mining-Based Algorithm to Detect Subtle Errors in Temperature Sensors in Gold Stripping Circuit. Mining, Metallurgy & Exploration 2020, 37, 459 -466.

AMA Style

Eduardo Pimenta De Melo, Rajive Ganguli, Rambabu Pothina. Modification and Enhanced Testing of Data Mining-Based Algorithm to Detect Subtle Errors in Temperature Sensors in Gold Stripping Circuit. Mining, Metallurgy & Exploration. 2020; 37 (2):459-466.

Chicago/Turabian Style

Eduardo Pimenta De Melo; Rajive Ganguli; Rambabu Pothina. 2020. "Modification and Enhanced Testing of Data Mining-Based Algorithm to Detect Subtle Errors in Temperature Sensors in Gold Stripping Circuit." Mining, Metallurgy & Exploration 37, no. 2: 459-466.

Article
Published: 17 January 2020 in Mining, Metallurgy & Exploration
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The economics of a mineral processing circuit is dependent on the numerous sensors that are critical to optimization and control systems. The sheer volume of sensors results in mines not being able to keep the sensors calibrated. When a sensor goes off calibration, it results in errors which can severely impact plant economics. Normally, these errors are not detected until the error grows and becomes a large (“gross”) error. Undetected errors are not remedied until the next calibration, which on average are a year apart Beamex (2019) [1] across all industries. Classical statistical methods that depend on linear relationships between input and response variables are typically used to find gross errors, but are not effective for highly non-linear and non-stationary operations in mining and mineral processing industries. Calibration of sensors is time-consuming and generally requires physical intervention that results in equipment downtimes causing production losses. Therefore, in situ detection methods are warranted that do not require physical removal of sensors to compare with standard measurements or well-calibrated sensors. Data-mining based techniques and algorithms have high success rate in tackling such problems. This paper presents results from a groundbreaking multi-year big data research project whose goal was to develop data-mining-based original techniques in a multi-sensor environment to identify when sensors start to stray, rather than wait for errors to grow. The carbon stripping (gold) circuit in the Pogo Mine of Alaska was selected for this project. Maintaining strip vessel temperatures at optimum levels is crucial for maximizing gold recoveries at Pogo, and hence the focus was on the temperature sensors of the two strip vessels. An automated algorithm to detect errors in strip vessel temperature sensors was developed that exploited data from multiple sensors in the strip circuit. This algorithm was able to detect with a high success rate when bias errors of magnitude as low as ± 2% were artificially induced into strip vessel temperature data streams. The detection times of about 1 month were much less than the industry average calibration frequency of 1 year. Thus, the algorithm helps detect errors early, without stopping the circuit. The algorithm alarms can serve as triggers when calibrations are done. Experimental methodology along with results, limitations of algorithms, and future research are also presented in this paper.

ACS Style

Rambabu Pothina; Rajive Ganguli. Detection of Subtle Sensor Errors in Mineral Processing Circuits Using Data-Mining Techniques. Mining, Metallurgy & Exploration 2020, 37, 399 -414.

AMA Style

Rambabu Pothina, Rajive Ganguli. Detection of Subtle Sensor Errors in Mineral Processing Circuits Using Data-Mining Techniques. Mining, Metallurgy & Exploration. 2020; 37 (2):399-414.

Chicago/Turabian Style

Rambabu Pothina; Rajive Ganguli. 2020. "Detection of Subtle Sensor Errors in Mineral Processing Circuits Using Data-Mining Techniques." Mining, Metallurgy & Exploration 37, no. 2: 399-414.

Review
Published: 11 May 2018 in MRS Energy & Sustainability
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New demand for electric vehicles—are rare earths the bottleneck in the supply chain? Can recycling and substitution make a dent in the demand for REE in the near future? Is it economically feasible for advanced nations to mine for REE but process them elsewhere to allay environmental concerns at home? Rare earths are critical components to many technologies that drive the modern world. Though rare earths are present in most parts of the world, they are produced mostly in China because of a confluence of several factors. This paper reviews various aspects of rare earths including extraction, geopolitics, and challenges. Rare-earth elements (REEs) not only replace each other in the mineral structure but also occur within different mineral structures in the same deposit. Separation of one REE from another is therefore difficult, environmentally challenging, and expensive. Less than 1% of REEs is recycled due to many challenges of collecting various end products and separating the REE from other metals/contaminants. Recycling investments have primarily focused on applications such as magnets, where economies of scale have allowed it. Substitution for the REE is difficult for most applications, though the automotive and wind energy industries are making good advances with motors and generators. The rare earth market is small and, thus, easily disrupted. Factors that can impact the market are increased production from existing mines, development of mine prospects advanced during price spikes, research and development efforts focused on improving REE recoveries, recycling, substitution, alternate sources of REEs, and governmental policies.

ACS Style

Rajive Ganguli; Douglas R. Cook. Rare earths: A review of the landscape. MRS Energy & Sustainability 2018, 5, 1 .

AMA Style

Rajive Ganguli, Douglas R. Cook. Rare earths: A review of the landscape. MRS Energy & Sustainability. 2018; 5 (1):1.

Chicago/Turabian Style

Rajive Ganguli; Douglas R. Cook. 2018. "Rare earths: A review of the landscape." MRS Energy & Sustainability 5, no. 1: 1.