Bringing Lunar LiDAR Back Down to Earth: Mapping Our Industrial Heritage through Deep Transfer Learning

This is the final version. Available on open access from MDPI via the DOI in this recordThis article presents a novel deep learning method for semi-automated detection of historic mining pits using aerial LiDAR data. The recent emergence of national scale remotely sensed datasets has created the potential to greatly increase the rate of analysis and recording of cultural heritage sites. However, the time and resources required to process these datasets in traditional desktop surveys presents a near insurmountable challenge. The use of artificial intelligence to carry out preliminary processing of vast areas could enable experts to prioritize their prospection focus; however, success so far has been hindered by the lack of large training datasets in this field. This study develops an innovative transfer learning approach, utilizing a deep convolutional neural network initially trained on Lunar LiDAR datasets and reapplied here in an archaeological context. Recall rates of 80% and 83% were obtained on the 0.5 m and 0.25 m resolution datasets respectively, with false positive rates maintained below 20%. These results are state of the art and demonstrate that this model is an efficient, effective tool for semi-automated object detection for this type of archaeological objects. Further tests indicated strong potential for detection of other types of archaeological objects when trained accordingly

Using deep learning and Hough transformations to infer mineralised veins from LiDAR data over historic mining areas

This is the final version. Available on open access from ISPRS via the DOI in this recordISPRS2020: XXIV ISPRS CongressThis paper presents a novel technique to improve geological understanding in regions of historic mining activity. This is achieved through inferring the orientations of geological structures from the imprints left on the landscape by past mining activities. Open source high resolution LiDAR datasets are used to fine-tune a deep convolutional neural network designed initially for Lunar LiDAR crater identification. By using a transfer learning approach between these two very similar domains, high accuracy predictions of pit locations can be generated in the form of a raster mask of pit location probabilities. Taking the raster of the predicted pit location centres as an input, a Hough transformation is used to fit lines through the centres of the detected pits. The results demonstrate that these lines follow the patterns of known mineralised veins in the area, alongside highlighting veins which are below the scale of the published geological maps

Factors associated with increased Emergency Department transfer in older long-term care residents: a systematic review

The proportion of adults older than 65 years is rapidly increasing. Care home residents in this age group have disproportionate rates of transfer to the Emergency Department (ED) and around 40% of attendances might be avoidable. We did a systematic review to identify factors that predict ED transfer from care homes. Six electronic databases were searched. Observational studies that provided estimates of association between ED attendance and variables at a resident or care home level were included. 26 primary studies met the inclusion criteria. Seven common domains of factors assessed for association with ED transfer were identified and within these domains, male sex, age, presence of specific comorbidities, polypharmacy, rural location, and care home quality rating were associated with likelihood of ED transfer. The identification of these factors provides useful information for policy makers and researchers intending to either develop interventions to reduce hospitalisations or use adjusted rates of hospitalisation as a care home quality indicator

Emergency Medicine Journal COVID-19 monthly top five

Following from the successful ‘RCEM weekly top five’ series starting in April 2020, this is the third of a monthly format for EMJ readers. We have undertaken a focused search of the PubMed literature using a standardised COVID-19 search string. Our search between 1 December and 31 December 2020 returned 1183 papers limited to human subjects and English language. We also searched high impact journals for papers of interest. https://emj.bmj.com/content/early/2021/02/11/emermed-2021-211203 This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. http://creativecommons.org/licenses/by-nc/4.0/ DOI http://dx.doi.org/10.1136/emermed-2021-21120

Evaluation of brittle fracturing in the sedimentary rock through laboratory analysis and computer simulation

This is the final version. Available from Faculty of Mining, Ecology, Process Control and Geotechnologies (FBERG), Technical University of Kosice via the DOI in this record. Estimation of the mechanical responses of a sample of rock is a critical characteristic to estimate the responses of rock strata under stress. In this paper, laboratory tests analysis and numerical modelling are used to analyse and replicate intact rock materials. Laboratory and petrographical analyses were undertaken to characterise the brittle response to the uniaxial loading of selected sedimentary samples. Complementary numerical modelling of virtual uniaxial compression tests is carried out using 3DEC software. These models were developed through a Grain Based Model capable of reproducing brittle failure of rocks, for which Voronoi 3D tessellation was generated. Failure mechanisms observed in laboratory and non-linear behaviour due to fracture propagation have been reproduced. Virtual modelling of intact rock with Discrete Element Code would allow, in combination with Discrete Fracture Networks, the numerical analysis of rock mass scale effects and anisotropy through Synthetic Rock Mass (SRM) modelling.Research Fund for Coal and Steel (RFCS)Polish Ministry of Science and Higher Educatio

What influences decisions to transfer older care-home residents to the emergency department? A synthesis of qualitative reviews

Background care home residents aged over 65 have disproportionate rates of emergency department (ED) attendance and hospitalisation. Around 40% attendances may be avoidable, and hospitalisation is associated with harms. We synthesised the evidence available in qualitative systematic reviews of different stakeholders’ experiences of decisions to transfer residents to the ED. Methods six electronic databases, references and citations of included reviews and relevant policy documents were searched. Reviews of qualitative studies exploring factors that influenced care home staff, medical practitioners, residents’ family or residents’ experiences and factors influencing decisions to transfer residents to the ED were included. Thematic analysis was used to synthesise findings. Results six previous reviews were included, which synthesised the findings of 34 primary studies encompassing 152 care home residents, 283 resident family members or carers and 447 care home staff. Of the primary studies, 19 were conducted in the North America, seven in Australia, five were conducted in Scandinavia, two in the United Kingdom and one in Holland. Three themes were identified: (i) power dynamics between residents, family members, care home staff and health care professionals (external to the care home) influence decisions; (ii) admission can be necessary; however, (iii) some decisions may be driven by factors other than clinical need. Conclusion transfer decisions are complex and are determined not just by changes in health status interventions aimed at reducing avoidable transfers need to address the key role family members have in transfer decisions, the medical legal fears of care home staff and barriers to accessing community services

Development of an Open Source Drillability Framework: Using Offshore Pile Top Large Diameter Drills as a case study

Offshore socket / shaft development is carried out from a specialised form of drilling utilising Pile Top Large Diameter Drills (PTLDD), and like all forms of offshore developments carry additional risks compared to the development of land-based infrastructure. Therefore, to reduce risks to drilling cost centres, estimation of the drills rate of penetration is required for project planning using empirical drillability models. At the time of writing, there is no drillability model to predict the performance of offshore PTLDDs. Machine learning methods of drillability have proved to increase accuracy of drillability estimates with rotary drills and Tunnel Boring Machines. In addition, the use of machine learning allows for automatic learning and model selection within confined boundaries, saving both time and increased accuracy. A drillability KDD process has been modified from the standard KDD process by Fayyad (1996). This process outlines the three key stages of development; Engineering Geological Model Creation, Drill mapping and machine learning. Each of these stages have been developed within the Python ecosystem allowing for open-source model creation and machine learning using SciKit Learn (Pedregosa et al., 2011). This workflow was applied to the historical PTLDD case studies which cover a range of geological terrains and engineering applications. This is the first time such as process has been applied to this niece style of drilling. The resulting performance estimations for the Rate of Penetration have been successfully estimated with a coefficient of determination (r2) of 0.7 from a combination of Hoek and Brown rock mass strength, unit weight, hole diameter, depth from drill and drill rig size. This model can be further improved by using a risk-based approach from the random sampling of geological parameters resulting in an r2 of 0.93. This model has the advantage of increasing observations from different realisations of the ground conditionsEngineering and Physical Sciences Research Council (EPSRC