System-of-Systems Considerations in the Notional Development of a Metropolitan Aerial Transportation System

There are substantial future challenges related to sustaining and improving efficient, cost-effective, and environmentally friendly transportation options for urban regions. Over the past several decades there has been a worldwide trend towards increasing urbanization of society. Accompanying this urbanization are increasing surface transportation infrastructure costs and, despite public infrastructure investments, increasing surface transportation "gridlock." In addition to this global urbanization trend, there has been a substantial increase in concern regarding energy sustainability, fossil fuel emissions, and the potential implications of global climate change. A recently completed study investigated the feasibility of an aviation solution for future urban transportation (refs. 1, 2). Such an aerial transportation system could ideally address some of the above noted concerns related to urbanization, transportation gridlock, and fossil fuel emissions (ref. 3). A metro/regional aerial transportation system could also provide enhanced transportation flexibility to accommodate extraordinary events such as surface (rail/road) transportation network disruptions and emergency/disaster relief responses

Ground-based hyperspectral imaging as a tool to identify different carbonate phases in natural cliffs

Recent research has shown hyperspectral imaging to be a powerful tool to distinguish carbonate phases with slight compositional differences on quarry cliff faces. The traditional remote sensing set5up uses an optimal short distance between the hyperspectral camera mounted on a tripod and a quarry wall characterized by a planar, mostly unweathered surface. Here we present results of a modified workflow geared to the application of ground5based hyperspectral imaging of rough and weathered cliff faces in order to map large scale dolomite bodies from a distance of up to several kilometres. The goal of the study was to determine unique spectral properties of fracture5controlled dolomite bodies in order to be able to distinguish them from a dolomitic host rock. In addition, the impact of weathering on carbonate phases and thus, the modification of the spectral signature between altered and unaltered carbonates is assessed. The spectral analysis is complemented by ICP5AES measurements of the spectrally measured powders. Furthermore, we examined the detection limits and characterisation potential of dolomite bodies from hyperspectral images captured at varying distances from cliff faces in the study area. Hyperspectral images of 10 natural cliffs distributed across the Central Oman Mountains were obtained with a Push broom scanner system. The high resolution of 5.45 nm (288 bands in total) enabled the visualization of small5scale changes in the near infrared continuous spectrum of all present lithofacies types. The determination of dolomite bodies of varying sizes (metre to hundreds of metres) on natural cliffs was achieved with the hyperspectral mapping approach and mapping results have been tested with the position of visually defined dolomite bodies on field panoramas. Spectra of natural cliffs contain a strong absorption peak indicative for iron which is absent in spectra of unweathered sample powders. However, ICP5AES analysis of powders revealed relatively low contents of iron of 12392 ppm. The strong peaks in field images are interpreted as linked to intensive weathering associated with the precepitation of goethite, hematite, specularite and manganese as well as intensive dedolomitization. Dedolomitization is indicated by calcitic spectra derived from the dolomite bodies. The spectral difference of laboratory and field spectra interferes significantly the application of laboratory spectra of powdered samples for the identification of dolomite bodies in the field. Furthermore, the process of late dedolomitization puts an additional challenge on the determination of dolomite bodies. Due to these strong spectral variations between laboratory and field spectra, we recommend that the mapping approaches should not solely rely on spectral algorithms but also consider normal light field panoramas and representative outcrop analysis. We also note that the quality of resolution is too low for the determination of small5scale variations of diagenetic phases at distances larger than 4 km. However, when the limitations mentioned are taken into account, hyperspectral imaging proves to be a powerful tool that helps in the determination of the distribution of diagenetic phases, even in challenging conditions

Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein

Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein.Comment: 4 pages, 8 Figures, Conference Submissio

Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein

The placenta protects the fetal circulation from anxiety-driven elevations in maternal serum levels of brain-derived neurotrophic factor.

Brain-derived neurotrophic factor (BDNF) plays crucial roles in brain function. Numerous studies report alterations in BDNF levels in human serum in various neurological conditions, including mood disorders such as depression. However, little is known about BDNF levels in the blood during pregnancy. We asked whether maternal depression and/or anxiety during pregnancy were associated with altered serum BDNF levels in mothers (n = 251) and their new-born infants (n = 212). As prenatal exposure to maternal mood disorders significantly increases the risk of neurological conditions in later life, we also examined the possibility of placental BDNF transfer by developing a new mouse model. We found no association between maternal symptoms of depression and either maternal or infant cord blood serum BDNF. However, maternal symptoms of anxiety correlated with significantly raised maternal serum BDNF exclusively in mothers of boys (r = 0.281; P = 0.005; n = 99). Serum BDNF was significantly lower in male infants than female infants but neither correlated with maternal anxiety symptoms. Consistent with this observation, we found no evidence for BDNF transfer across the placenta. We conclude that the placenta protects the developing fetus from maternal changes in serum BDNF that could otherwise have adverse consequences for fetal development

Quantifying the sensitivity of distributive fluvial systems to changes in sediment supply and lake level using stratigraphic forward modelling

Stratigraphic forward modelling has been used to quantify the sensitivity of sandbody connectivity in a distributive fluvial system to changes in sediment supply and lake level. Recent stratigraphic forward modelling using SedsimX from StrataMod Pty Limited of the Oligocene to Miocene Huesca distributive fluvial system in northern Spain was used as a base‐case for this sensitivity analysis. Based on literature research and initial modelling, a sediment supply sensitivity range of 0.22 to 21.85 km3/kyr and lake‐level sensitivity range of −1000 to 1000 mm/kyr were used. Results show that the stratigraphic architecture of the modelled distributive fluvial system is more sensitive to changes in sediment supply than to changes in lake level. While an increase in the rate of sediment supply results in an increase in preserved average grain size, aggradation rates and sandbody connectivity at the same distance from the apex, the average grain size, aggradation rate and sandbody connectivity all decrease with increasing distance from fan apex. The main difference in the stratigraphic architecture can be found in the proximal zones. Only oversupplied models, with much higher sediment supply than the base‐case, deposited fully amalgamated channelized deposits with laterally continuous, tabular beds with occasional scoured surfaces. Models with base‐case sediment supply contain channelized sandy deposits within a fine‐grained floodplain environment. Models with sediment supply much lower than the base‐case had no deposition in the proximal zone. Lake‐level rise leads to reduced distal erosion of sediments, concentration of silts close to the lake shore, and higher aggradation rates and thicker sandbodies in the proximal zone. The sensitivity analysis highlights that the parameters governing the formation of distributive fluvial systems have different weightings but are ultimately all interconnected and interdependent. This quantitative framework can be used as a predictive tool for subsurface exploration in distributive fluvial systems

A unified multiwavelength model of galaxy formation

We present a new version of the GALFORM semi-analytical model of galaxy formation. This brings together several previous developments of GALFORM into a single unified model, including a different initial mass function (IMF) in quiescent star formation and in starbursts, feedback from active galactic nuclei supressing gas cooling in massive halos, and a new empirical star formation law in galaxy disks based on their molecular gas content. In addition, we have updated the cosmology, introduced a more accurate treatment of dynamical friction acting on satellite galaxies, and updated the stellar population model. The new model is able to simultaneously explain both the observed evolution of the K-band luminosity function and stellar mass function, and the number counts and redshift distribution of sub-mm galaxies selected at 850μm. This was not previously achieved by a single physical model within the ΛCDM framework, but requires having an IMF in starbursts that is somewhat top-heavy. The new model is tested against a wide variety of observational data covering wavelengths from the far-UV to sub-mm, and redshifts from z = 0 to z = 6, and is found to be generally successful. These observations include the optical and near-IR luminosity functions, HI mass function, fraction of early type galaxies, Tully-Fisher, metallicity-luminosity and size-luminosity relations at z = 0, as well as far-IR number counts, and far-UV luminosity functions at z ∼ 3 − 6. Discrepancies are however found in galaxy sizes and metallicities at low luminosities, and in the abundance of low mass galaxies at high-z, suggesting the need for a more sophisticated model of supernova feedback