On the diversification benefits of cryptocurrencies

Whether cryptocurrencies are to be considered as a means of payment or a speculative asset and whether they are best employed as a diversifier or hedge is still a vexata quaestio. In this paper I question the diversification abilities of cryptocurrencies by adding them to portfolios of different instruments and comparing the performance of the portfolios with and without cryptocurrencies. At the end I add transaction costs to see if, after accounting for them, the same results would hold. I find that adding cryptocurrencies does not improve diversification. For portfolios formed with the 1/N and optimized Sharpe ratio strategies adding cryptocurrencies does not reduce risks but it improves the risk return tradeoff. Even after accounting for transaction costs, this improvement remains. For the global minimum variance portfolios adding cryptocurrencies reduces risks but does not improve the risk return tradeoff.Se as moedas criptográficas devem ser consideradas como um meio de pagamento ou um ativo especulativo e se são melhor empregues como um diversificador ou uma cobertura é ainda uma vexata quaestio. Neste artigo questiono as capacidades de diversificação das moedas criptográficas ao adicioná­las às carteiras de diferentes instrumentos e ao comparar o desempenho das carteiras com e sem moedas criptográficas. No final, adiciono custos de transação para ver se, após a contabilização dos mesmos, os mesmos resultados se manteriam. Concluo que adicionar moedas criptográficas não aumenta a diversificação. Para as carteiras formadas com as estratégias de rácio 1/N e Sharpe otimizado, adicionar moedas criptográficas não reduz os riscos, mas melhora o tradeoff risco­retorno. Mesmo após a contabilização dos custos de transação, esta melhoria mantém­se. Para as carteiras de variação mínima global, a adição de moedas criptográficas reduz os riscos mas não aumenta o tradeoff risco­retorno

A phenomenological study of foster carers\u27 experiences of formal and informal support

Foster children are showing a higher prevalence of maladaptive physical and psychosocial issues than ever before. The presence of these issues is predictive of foster placement instability, which is compounded by the inability to recruit and retain foster carers. As placement disruption can have numerous consequences, the factors that influence placement stability have been reviewed. Carer strain is a widespread destabilizing factor, which is augmented by many factors including the perceived level of practical and emotional support from both formal and informal networks. Formal support is linked to placement stability, although carers generally feel undervalued and unappreciated by formal networks. Alternatively, informal networks enhance carer psychosocial wellbeing and improve placement stability. However from the literature reviewed, there appear to be a limited understanding of the influence that both formal and informal support networks have on a foster carers\u27 ability to provide a nurturing foster home. The number of children in foster care has increased significantly, which is compounded by the inability to recruit and retain carers. Previous research has shown that formal and informal support can improve carer retention, although little research has explored this in Australia. The present study used a phenomenological approach with seven carers through semi-structured interviews. Thematic analysis indicated that carers derived satisfaction from fostering, although this was hindered by child behaviour and biological parents. Carers also felt unsupported and unappreciated by formal networks, which manifested through issues such as: inadequate child-information, irregular contact, exclusion from decision-making and unacknowledged attachments during placement termination. With informal support, carers described feeling socially restricted and often received criticism, although some carers received positive responses from informal networks, and emphasised the need for contact with other carers. These findings highlight the importance of formal and informal support in reducing carer strain and improving carer retention

Psychophysiological research of borderline personality disorder: Review and implications for biosocial theory

According to the Biosocial theory, Borderline Personality Disorder (BPD) is developed by a biological predisposition to hyperarousal and hyperreactivity combined with an invalidating environment. Although widely supported by subjective measures, the impaired insight present in BPD may skew results, and thus psychophysiological measures have been suggested as an alternative method of examining possible biological differences in BPD. The current review aimed to critically assess psychophysiological research of BPD by electronic searching of relevant databases, with 22 articles meeting inclusion criteria. Results showed that in contrast to the hyperarousal proposed in the Biosocial theory, BPD was associated with hypoarousal and hyporeactivity to non-emotionally valenced stimuli. However, there was also evidence of BPD hyperreactivity towards negatively valenced stimuli, and impaired habituation during stressor tasks. As current psychophysiological results were inconsistent, it has been postulated that there may be possible subtypes of BPD. Further, evolutionary-based theories do not appear to adequately explain the complexity of emotion dysregulation in BPD, thus the Emotional Coherence theory has been proposed as an alternate method of conceptualising the role of psychophysiology in BPD. From the lack of clear or consistent findings, further research in the area appears necessary to determine the role of psychophysiology in BPD

Impact of Climate Change on the Heating Demand of Buildings. A District Level Approach

There is no doubt that during recent years, the developing countries are in urgent demand of energy, which means the energy generation and the carbon emissions increase accumulatively. The 40 % of the global energy consumption per year comes from the building stock. Considering the predictions regarding future climate due to climate change, a good understanding on the energy use due to future climate is required. The aim of this study was to evaluate the impact of future weather in the heating demand and carbon emissions for a group of buildings at district level, focusing on two areas of London in the United Kingdom. The methodological approach involved the use of geospatial data for the case study areas, processed with Python programming language through Anaconda and Jupyter notebook, generation of an archetype dataset with energy performance data from TABULA typology and the use of Python console in QGIS to calculate the heating demand in the reference weather data, 2050 and 2100 in accordance with RCP 4.5 and RCP 8.5 scenarios. A validated model was used for the district level heating demand calculation. On the one hand, the results suggest that a mitigation of carbon emissions under the RCP4.5 scenario will generate a small decrease on the heating demand at district level, so slightly similar levels of heating generation must continue to be provided using sustainable alternatives. On the other hand, following the RCP 8.5 scenario of carbon emission carrying on business as usual will create a significant reduction of heating demand due to the rise on temperature but with the consequent overheating in summer, which will shift the energy generation problem. The results suggest that adaptation of the energy generation must start shifting to cope with higher temperatures and a different requirement of delivered energy from heating to cooling due to the effect of climate change

Evaluating the Influence of Program Type Building Parameters on UBEM: A Case Study for the Residential Stock in Nottingham, UK

In the midst of rising concern about the implications of climate change, the European Union and the United Kingdom appears to be on the verge of establishing policies to reduce greenhouse gas emissions. The urban building energy models could inform energy analyzers and decision makers for the future results that specific comprehensive energy refurbishment strategies and energy supply infrastructure changes might have. Nonetheless, the data challenges that emerge are various. The lack of data availability and reliability, the data computing issue and data privacy are, only, some of the challenges of building energy modelling, which are intensified in urban scale. Therefore, the investigation of the influence of building parameters on the energy demand results is deemed necessary, in order both to understand the minimum data requirements for urban energy modelling, and the impact of them before the design phase for the new constructions. Therefore, this Paper’s intention is to inform stakeholders from energy analysts to data capture companies, about the influential building parameters, as regards to the Program Type, such as the infiltration, the domestic hot water and the ventilation. An UBEM physics-based approach, for the estimation of the annual energy demand, is implemented with the use of Grasshopper software, and the visualization of the results is done with the QGIS software. The case study is in Nottingham city, in UK, and the energy demand for the whole year of the dwelling stock is estimated. Then, a sensitivity analysis for the influence of the Program Type building parameters is presented. The results have shown that the most impactful parameter among the three under-tested is the infiltration (airtightness) of a dwellin

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research

Sustainable urban development indicators in Great Britain from 2001 to 2016

Current planning strategies promoting suburbanisation, land use zoning and low built-up density areas tend to increase the environmental footprint of cities. In the last decades, international and local government plans are increasingly targeted at making urban areas more sustainable. Urban structure has been proved to be an important factor guiding urban smart growth policies that promote sustainable urban environments and improve neighbourhood social cohesion. This paper draws on a series of unique historical datasets obtained from Ordnance Survey, covering the largest British urban areas over the last 15 years (2001–2016) to develop a set of twelve indicators and a composite Sustainable Urban Development Index to quantitatively measure and assess key built environment features and their relative change compared to other areas at each point in time based on regular 1 km2 grids. The results show that there is a relative increase in urban structure sustainability of areas in and around city centres and identify that the primary built environment feature driving these improvements was an increase in walkable spaces

What can we expect from navigating? Exploring navigation, wearables and data through critical design concepts

What is it to navigate or to be navigated? How, and through what, is information communicated to us? Do our interactions with space need to be limited to when we are moving through it? This paper describes a collection of design concepts generated as part of the initial stages of a research project that combines a critical design mindset and research through design process to explore these types of questions. The project seeks to problematise and diversify the discussion and understanding around pedestrian navigation, wearable technology, crowdsourcing and human data interaction. The goal is to develop one of the concepts using research through design as part of PhD research studies, leading to possible future applications

Spatial scale analysis of landscape processes for digital soil mapping in Ireland

Soil is one of the most precious resources on Earth because of its role in storing and recycling water and nutrients essential for life, providing a variety of ecosystem services. This vulnerable resource is at risk from degradation by erosion, salinity, contamination and other effects of mismanagement. Information from soil is therefore crucial for its sustainable management. While the demand for soil information is growing, the quantity of data collected in the field is reducing due to financial constraints. Digital Soil Mapping (DSM) supports the creation of geographically referenced soil databases generated by using field observations or legacy data coupled, through quantitative relationships, with environmental covariates. This enables the creation of soil maps at unexplored locations at reduced costs. The selection of an optimal scale for environmental covariates is still an unsolved issue affecting the accuracy of DSM. The overall aim of this research was to explore the effect of spatial scale alterations of environmental covariates in DSM. Three main targets were identified: assessing the impact of spatial scale alterations on classifying soil taxonomic units; investigating existing approaches from related scientific fields for the detection of scale patterns and finally enabling practitioners to find a suitable scale for environmental covariates by developing a new methodology for spatial scale analysis in DSM. Three study areas, covered by detailed reconnaissance soil survey, were identified in the Republic of Ireland. Their different pedological and geomorphological characteristics allowed to test scale behaviours across the spectrum of conditions present in the Irish landscape. The investigation started by examining the effects of scale alteration of the finest resolution environmental covariate, the Digital Elevation Model (DEM), on the classification of soil taxonomic units. Empirical approaches from related scientific fields were subsequently selected from the literature, applied to the study areas and compared with the experimental methodology. Wavelet analysis was also employed to decompose the DEMs into a series of independent components at varying scales and then used in DSM analysis of soil taxonomic units. Finally, a new multiscale methodology was developed and evaluated against the previously presented experimental results. The results obtained by the experimental methodology have proved the significant role of scale alterations in the classification accuracy of soil taxonomic units, challenging the common practice of using the finest available resolution of DEM in DSM analysis. The set of eight empirical approaches selected in the literature have been proved to have a detrimental effect on the selection of an optimal DEM scale for DSM applications. Wavelet analysis was shown effective in removing DEM sources of variation, increasing DSM model performance by spatially decomposing the DEM. Finally, my main contribution to knowledge has been developing a new multiscale methodology for DSM applications by combining a DEM segmentation technique performed by k-means clustering of local variograms parameters calculated in a moving window with an experimental methodology altering DEM scales. The newly developed multiscale methodology offers a way to significantly improve classification accuracy of soil taxonomic units in DSM. In conclusion, this research has shown that spatial scale analysis of environmental covariates significantly enhances the practice of DSM, improving overall classification accuracy of soil taxonomic units. The newly developed multiscale methodology can be successfully integrated in current DSM analysis of soil taxonomic units performed with data mining techniques, so advancing the practice of soil mapping. The future of DSM, as it successfully progresses from the early pioneering years into an established discipline, will have to include scale and in particular multiscale investigations in its methodology. DSM will have to move from a methodology of spatial data with scale to a spatial scale methodology. It is now time to consider scale as a key soil and modelling attribute in DSM