Home Values and Firm Behavior

The homes of firm owners are an important source of finance for ongoing businesses. We use UK microdata to show that a £1 increase in the value of the homes of a firm's directors increases the firm's investment by £0.03. This effect is concentrated among firms whose directors' homes are valuable relative to the firm's assets, that are financially constrained, and that have directors who are personally highly levered. An aggregation exercise shows that directors' homes are as important as corporate property for collateral driven fluctuations in aggregate investment demand

Electricity consumption and household characteristics: Implications for census-taking in a smart metered future

This paper assesses the feasibility of determining key household characteristics based on temporal load profiles of household electricity demand. It is known that household characteristics, behaviours and routines drive a number of features of household electricity loads in ways which are currently not fully understood. The roll out of domestic smart meters in the UK and elsewhere could enable better understanding through the collection of high temporal resolution electricity monitoring data at the household level. Such data affords tremendous potential to invert the established relationship between household characteristics and temporal load profiles. Rather than use household characteristics as a predictor of loads, observed electricity load profiles, or indicators based on them, could instead be used to impute household characteristics. These micro level imputed characteristics could then be aggregated at the small area level to produce ‘census-like’ small area indicators. This work briefly reviews the nature of current and future census taking in the UK before outlining the household characteristics that are to be found in the UK census and which are also known to influence electricity load profiles. It then presents descriptive analysis of two smart meter-like datasets of half-hourly domestic electricity consumption before reporting on the results from a multilevel modelling-based analysis of the same data. The work concludes that a number of household characteristics of the kind to be found in UK census-derived small area statistics may be predicted from particular load profile indicators. A discussion of the steps required to test and validate this approach and the wider implications for census taking is also provided

Recent Developments in Detection of Central Serous Retinopathy through Imaging and Artificial Intelligence Techniques – A Review

Central Serous Retinopathy (CSR) or Central Serous Chorioretinopathy (CSC) is a significant disease that causes blindness and vision loss among millions of people worldwide. It transpires as a result of accumulation of watery fluids behind the retina. Therefore, detection of CSR at early stages allows preventive measures to avert any impairment to the human eye. Traditionally, several manual methods for detecting CSR have been developed in the past; however, they have shown to be imprecise and unreliable. Consequently, Artificial Intelligence (AI) services in the medical field, including automated CSR detection, are now possible to detect and cure this disease. This review assessed a variety of innovative technologies and researches that contribute to the automatic detection of CSR. In this review, various CSR disease detection techniques, broadly classified into two categories: a) CSR detection based on classical imaging technologies, and b) CSR detection based on Machine/Deep Learning methods, have been reviewed after an elaborated evaluation of 29 different relevant articles. Additionally, it also goes over the advantages, drawbacks and limitations of a variety of traditional imaging techniques, such as Optical Coherence Tomography Angiography (OCTA), Fundus Imaging and more recent approaches that utilize Artificial Intelligence techniques. Finally, it is concluded that the most recent Deep Learning (DL) classifiers deliver accurate, fast, and reliable CSR detection. However, more research needs to be conducted on publicly available datasets to improve computation complexity for the reliable detection and diagnosis of CSR disease

Identifying patients with PTSD utilizing resting-state fMRI data and neural network approach

Purpose: The primary aim of the study is to identify the existence of the post-traumatic stress disorder (PTSD) in an individual and to detect the dominance level of each affected brain region in PTSD using rs-fMRI data. This will assist the psychiatrists and neurologists to distinguish impartially between PTSD individuals and healthy controls for the brain-based treatment of PTSD. Methods: Twenty-eight individuals (14 with PTSD, 14 healthy controls) were assessed to obtain rs-fMRI data of their six brain regions-of-interest. The rs-fMRI data analyzed by the Artificial Neural Network (ANN), adopting the training-validation-testing approach to classify PTSD and to identify the most affected brain region due to PTSD. The classification accuracy is justified by a variety of different methods and metrics. Results: Three ANN models were established to attain the study’s purpose using the susceptible regions in the right, left, and both hemispheres, and the classification accuracy of ANN models achieved 79%, 93.5%, and 94.5%, respectively. The prediction accuracy even increased in the independent holdout sample using trained models. The developed models are reliable, intellectually attractive and generalize. Additionally, the most dominant region in the PTSD individuals was the left hippocampus and the least was the right hippocampus. Conclusion: The present investigation achieved high classification accuracy and identified the brain regions those contributed most to differentiating PTSD individuals from healthy controls. The results indicated that the left hippocampus is the most affected brain region in PTSD individuals. Therefore, our findings are helpful for practitioners for diagnostic, medication, and therapy of the affected brain regions by knowing the strength of infected regions

Current tidal power technologies and their suitability for applications in coastal and marine areas

A considerable body of research is currently being performed to quantify available tidal energy resources and to develop efficient devices with which to harness them. This work is naturally focussed on maximising power generation from the most promising sites, and a review of the literature suggests that the potential for smaller scale, local tidal power generation from shallow near-shore sites has not yet been investigated. If such generation is feasible, it could have the potential to provide sustainable electricity for nearby coastal homes and communities as part of a distributed generation strategy, and would benefit from easier installation and maintenance, lower cabling and infrastructure requirements and reduced capital costs when compared with larger scale projects. This article reviews tidal barrages and lagoons, tidal turbines, oscillating hydrofoils and tidal kites to assess their suitability for small-scale electricity generation in shallow waters. This is achieved by discussing the power density, scalability, durability, maintainability, economic potential and environmental impacts of each concept. The performance of each technology in each criterion is scored against axial-flow turbines, allowing for them to be ranked according to their overall suitability. The review suggests that tidal kites and range devices are not suitable for small-scale shallow water applications due to depth and size requirements respectively. Cross-flow turbines appear to be the most suitable technology, as they have high power densities and a maximum size that is not constrained by water depth

How sharing can contribute to more sustainable cities

\ua9 2017 by the authors. Recently, much of the literature on sharing in cities has focused on the sharing economy, in which people use online platforms to share underutilized assets in the marketplace. This view of sharing is too narrow for cities, as it neglects the myriad of ways, reasons, and scales in which citizens share in urban environments. Research presented here by the Liveable Cities team in the form of participant workshops in Lancaster and Birmingham, UK, suggests that a broader approach to understanding sharing in cities is essential. The research also highlighted tools and methods that may be used to help to identify sharing in communities. The paper ends with advice to city stakeholders, such as policymakers, urban planners, and urban designers, who are considering how to enhance sustainability in cities through sharing