The synthetic geo-ecological environmental evaluation of a coastal coal-mining city using spatiotemporal big data: A case study in Longkou, China

Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease monitoring and so on. Thus, most of the existing systems portray some limitations at various levels of processing and implementation. Some of the most commonly observed factors involve lack of reliability, scalability and exceeding computational costs. In this paper, we address different well-known scalable serverless frameworks i.e., Amazon Web Services (AWS) Lambda, Google Cloud Functions and Microsoft Azure Functions for the management of geospatial big data. We discuss some of the existing approaches that are popularly used in analyzing geospatial big data and indicate their limitations. We report the applicability of our proposed framework in context of Cloud Geographic Information System (GIS) platform. An account of some state-of-the-art technologies and tools relevant to our problem domain are discussed. We also visualize performance of the proposed framework in terms of reliability, scalability, speed and security parameters. Furthermore, we present the map overlay analysis, point-cluster analysis, the generated heatmap and clustering analysis. Some relevant statistical plots are also visualized. In this paper, we consider two application case-studies. The first case study was explored using the Mineral Resources Data System (MRDS) dataset, which refers to worldwide density of mineral resources in a country-wise fashion. The second case study was performed using the Fairfax Forecast Households dataset, which signifies the parcel-level household prediction for 30 consecutive years. The proposed model integrates a serverless framework to reduce timing constraints and it also improves the performance associated to geospatial data processing for high-dimensional hyperspectral data.

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Habemus Corpora: Reapproaching Philological Problems in the Age of ‘Big’ Data

Anglia - Zeitschrift für englische Philologie ◽

10.1515/ang-2021-0006 ◽

2021 ◽

Vol 139 (1) ◽

pp. 94-127

Author(s):

Mark Faulkner

Keyword(s):

Big Data ◽

Old English ◽

English Language ◽

Historical Research ◽

British Library ◽

New Techniques ◽

Major Task ◽

New Methodologies ◽

Primary Focus

Abstract This paper demonstrates the potential of new methodologies for using existing corpora of medieval English to better contextualise linguistic variants, a major task of philology and a key underpinning of our ability to answer major literary-historical questions, such as when, where and to what purpose medieval texts and manuscripts were produced. The primary focus of the article is the assistance these methods can offer in dating the composition of texts, which it illustrates with a case study of the “Old” English Life of St Neot, uniquely preserved in the mid-twelfth-century South-Eastern homiliary, London, British Library, Cotton Vespasian D.xiv, fols. 4–169. While the Life has recently been dated around 1100, examining its orthography, lexis, syntax and style alongside that of all other English-language texts surviving from before 1150 using new techniques for searching the Dictionary of Old English Corpus suggests it is very unlikely to be this late. The article closes with some reflections on what book-historical research should prioritise as it further evolves into the digital age.

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Occupational injuries in underground coal mining in Serbia: A case study

Work ◽

10.3233/wor-213514 ◽

2021 ◽

pp. 1-11

Author(s):

Ivana Ilić Krstić ◽

Danijela Avramović ◽

Snežana Živković

Keyword(s):

Coal Mining ◽

Occupational Safety ◽

Preventive Measures ◽

Occupational Injuries ◽

Underground Coal Mining ◽

Health Records ◽

Employee Safety ◽

Injury Data ◽

Safety And Health

BACKGROUND: Mining, especially underground coal mining, has always been a hazardous occupation. Injuries, including those that are fatal, are a major occupational risk that all miners have to face. OBJECTIVE: Despite the fact that all workers are aware of the risks, efforts must be made to increase their safety through the implementation of preventive measures. METHODS: This retrospective study includes injury data from all nine Serbian coal mines over a 16-year period, from 2000 to 2016. All injury data were collected from employee safety and health records. RESULTS: In the analyzed period, a total of 9,273 occupational injuries were recorded at Resavica. The highest number of occupational injuries (over 600) were recorded in 2008 (669), 2004 (651), and 2005 (603). The data shows that almost one fifth of the total number of occupational injuries, or 20.74%, occurred during the said three years. On average, 493.06 minor, 51.12 severe, and 1.29 fatal injuries occurred each year. Every day there were 1.29 minor occupational injuries and every seven days there was one severe injury. CONCLUSIONS: Despite indications that there is a connection between some of the variables and the number of injuries, the general conclusion is that injuries are accidental and unpredictable. The high percentages of injuries are due to the organization of work and the age or qualification structure of the staff and, of course, are the consequence of likelihood –the greater the number of workers, the greater the likelihood of injury. However, the present study showed that some measures can be taken to increase occupational safety and reduce the number of injuries.

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