Tracking a city’s center of gravity over 500 years of growth from a time series of georectified historical maps

\ua9 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. It is surprising difficult to define where a city center lies, yet its location has a profound effect on a city’s structure and function. We examine whether city center typicality points can be consistently located on historical maps such that their centroid identifies a meaningful central location over a 500-year period in Southampton, UK. We compare movements of this city center centroid against changes in the geographical center of the city as defined by its boundary. Southampton’s historical maps were georectified with a mean accuracy of 21 m (range 9.9 to 47 m), and 18 to 102 typicality points were identified per map, enough to chart changes in the city center centroid through time. Over nearly 500 years, Southampton’s center has moved just 343 m, often corresponding with the key retail attractants of the time, while its population has increased 80-fold, its administrative area 60-fold and its geographical center moved 1985 m. This inertia to change in the city center presents environmental challenges for the present-day, made worse by the geography of Southampton, bounded by the sea, rivers and major roads. Geographical context, coupled with planning decisions in the past that maintain a city center in its historical location, place limits on the current sustainability of a city

Spatial variation in sound frequency components across an urban area derived from mobile surveys

\ua9 2019 The Author(s). Continuous exposure to noise can lead to premature hearing loss, reduced cognitive performance, insomnia, stress, hypertension, cardiovascular diseases and stroke. Road noise affects the health of >125 million people in the European Union and Member States are required to map major noise hotspots. These strategic noise maps are usually derived from traffic counts and propagation models because large- scale measurement of the acoustic environment using conventional methods is infeasible. In this study, the authors surveyed the entire city of Southampton, UK using a mobile survey technique, capturing spatial variations in street- level sound characteristics across multiple frequencies from all sound sources. Over 52,000 calibrated and georeferenced sound clips covering 11 Hz to 22.7 kHz are analysed here to investigate variations in sound frequency composition across urban space and then applied to two issues: the definition of naturalness in the acoustic environment; and perceptions of social inequity in sound exposure. Clusters of acoustic characteristics were identified and mapped using spectral clustering and principal components analysis based on octave bands, ecoacoustic indices and dBA. We found independent patterns in low, mid and high frequencies, and the ecoacoustic indices that related to land use. Ecoacoustic indices partially mapped onto greenspace, identifying naturalness, but not uniquely, probably because urban anthropogenic sounds occur at higher frequencies than in the natural areas where such indices were developed. There was some evidence of inequity in sound exposure according to social deprivation and ethnicity, and results differed according to frequency bands. The consequences of these findings and the benefits of city-wide sound surveys for urban planning are discussed

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

The effect of tidal flow directionality on tidal turbine performance characteristics

With many Tidal Energy Conversion (TEC) devices at full scale prototype stage there are two distinct design groups for Horizontal Axis Tidal Turbines (HATTs). Devices with a yaw mechanism allowing the turbine to always face into the flow, and devices with blades that can rotate through 180° to harness a strongly bi-directional flow. As marine turbine technology verges on the realm of economic viability this paper reveals the performance of Cardiff University's concept tidal turbine with its support structure either upstream or downstream and with various proximities between the rotating plane of the turbine and its support stanchion. Through the use of validated Computational Fluid Dynamics (CFD) modelling this work shows the optimal proximity between rotor plane and stanchion as well as establishing, in the given context, the use of a yaw mechanism to be superior to a bi-directional system from a performance perspective

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

Internet of things (IoT) assisted soil salinity mapping at irrigation schema level

Soil salinity accumulates a high concentration of salts in soils that interfere with normal plant growth. Early detection and quantification of soil salinity are essential to effectively deal with soil salinity in agriculture. Soil salinity quantification and mapping at the irrigation scheme level are vital to evaluating saline soil's reclamation activity. Existing solutions of salinity mapping are costly, time-consuming, and inadequate for applications at the irrigation scheme level. Internet of Things (IoT) assisted salinity mapping at the irrigation scheme level is proposed to quantify and map the soil salinity in agriculture. The proposed IoT-assisted salinity mapping characterizes the soil salinity in terms of Electric Conductivity, pH, and Total Dissolved Salts. The proposed IoT-assisted salinity mapping effectively observes impacts of reclamation activities in saline soil by frequent observation of soil salinity cost-effectively. The accuracy of proposed IoT-assisted salinity mapping is evaluated against the standard method of salinity measurements. The proposed IoT-assisted salinity mapping is cost-effective, and portable, which is very useful for site-specific treatments and soil zones management in saline soils

Kinetic energy extraction of a tidal stream turbine and its sensitivity to structural stiffness attenuation

© 2015 The Authors. The hydrodynamic forces imparted on a tidal turbine rotor, whilst causing it to rotate and hence generate power, will also cause the blades to deform. This deformation will affect the turbine's performance if not included in the early design phase and could lead to a decrease in power output and a reduction in operational life. Conversely, designing blades to allow them to deform slightly may reduce localised stress and therefore prolong the life of the blades and allow the blades to deform in to their optimum operational state. The aim of this paper is to better understand the kinetic energy extraction by varying the material modulus of a turbine blade. Shaft torque/power, blade tip displacement, and axial thrust results are presented for 2, 3 and 4 bladed rotor configurations at peak power extraction. For the rotor design studied the FSI model data show that there is a low sensitivity to blade deformation for the 2, 3 and 4 bladed rotors. However, the results reveal that the 3 bladed rotor displayed maximum hydrodynamic performance as a rigid structure which then decreased as the blade deformed. The 2 and 4 bladed rotor configurations elucidated a slight increase in hydrodynamic performance with deflection

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