A Biomimetic Model of the Outer Plexiform Layer by Incorporating Memristive Devices

In this paper we present a biorealistic model for the first part of the early vision processing by incorporating memristive nanodevices. The architecture of the proposed network is based on the organisation and functioning of the outer plexiform layer (OPL) in the vertebrate retina. We demonstrate that memristive devices are indeed a valuable building block for neuromorphic architectures, as their highly non-linear and adaptive response could be exploited for establishing ultra-dense networks with similar dynamics to their biological counterparts. We particularly show that hexagonal memristive grids can be employed for faithfully emulating the smoothing-effect occurring at the OPL for enhancing the dynamic range of the system. In addition, we employ a memristor-based thresholding scheme for detecting the edges of grayscale images, while the proposed system is also evaluated for its adaptation and fault tolerance capacity against different light or noise conditions as well as distinct device yields

Investigating the feasibility of vehicle telemetry data as a means of predicting driver workload

Driving is a safety critical task that requires a high level of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimize any further demand. Traditionally, workload measurements have been performed using intrusive means such as physiological sensors. Another approach may be to use vehicle telemetry data as a performance measure for workload. In this paper, we present the Warwick-JLR Driver Monitoring Dataset (DMD) and analyse it to investigate the feasibility of using vehicle telemetry data for determining the driver workload. We perform a statistical analysis of subjective ratings, physiological data, and vehicle telemetry data collected during a track study. A data mining methodology is then presented to build predictive models using this data, for the driver workload monitoring problem

Data mining for vehicle telemetry

This paper presents a data mining methodology for driving condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labelling problems: Road Type (A, B, C and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, namely, signal selection, feature extraction, and feature selection. The selection methods used include Principal Components Analysis (PCA) and Mutual Information (MI), which are used to determine the relevance and redundancy of extracted features, and are performed in various combinations. Finally, as there is an inherent bias towards certain road and carriageway labellings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension heigh

Effects of Feeding Bt Maize to Sows during Gestation and Lactation on Maternal and Offspring Immunity and Fate of Transgenic Material

peer-reviewedBackground: We aimed to determine the effect of feeding transgenic maize to sows during gestation and lactation on maternal and offspring immunity and to assess the fate of transgenic material. Methodology/Principal Findings: On the day of insemination, sows were assigned to one of two treatments (n = 12/treatment); 1) non-Bt control maize diet or 2) Bt-MON810 maize diet, which were fed for ~143 days throughout gestation and lactation. Immune function was assessed by leukocyte phenotyping, haematology and Cry1Ab-specific antibody presence in blood on days 0, 28 and 110 of gestation and at the end of lactation. Peripheral-blood mononuclear cell cytokine production was investigated on days 28 and 110 of gestation. Haematological analysis was performed on offspring at birth (n = 12/treatment). Presence of the cry1Ab transgene was assessed in sows' blood and faeces on day 110 of gestation and in blood and tissues of offspring at birth. Cry1Ab protein presence was assessed in sows' blood during gestation and lactation and in tissues of offspring at birth. Blood monocyte count and percentage were higher (P<0.05), while granulocyte percentage was lower (P<0.05) in Bt maize-fed sows on day 110 of gestation. Leukocyte count and granulocyte count and percentage were lower (P<0.05), while lymphocyte percentage was higher (P<0.05) in offspring of Bt maize-fed sows. Bt maize-fed sows had a lower percentage of monocytes on day 28 of lactation and of CD4+CD8+ lymphocytes on day 110 of gestation, day 28 of lactation and overall (P<0.05). Cytokine production was similar between treatments. Transgenic material or Cry1Ab-specific antibodies were not detected in sows or offspring. Conclusions/Significance: Treatment differences observed following feeding of Bt maize to sows did not indicate inflammation or allergy and are unlikely to be of major importance. These results provide additional data for Bt maize safety assessment.The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007–2013) under grant agreement number 211820 and the Teagasc Walsh Fellowship Programme

Warwick-JLR driver monitoring dataset (DMD) : statistics and early findings

Driving is a safety critical task that requires a high levels of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimize any further demand. Traditionally, workload measurements have been performed using intrusive means such as physiological sensors. Another approach may be to monitor workload online using readily available and robust sensors accessible via the vehicle's Controller Area Network (CAN). In this paper, we present details of the Warwick-JLR Driver Monitoring Dataset (DMD) collected for this purpose, and to announce its publication for driver monitoring research. The collection protocol is briefly introduced, followed by statistical analysis of the dataset to describe its structure. Finally, the public release of the dataset, for use in both driver monitoring and data mining research, is announced

Data mining for vehicle telemetry

This article presents a data mining methodology for driving-condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems, and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labeling problems: Road Type (A, B, C, and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, that is, signal selection, feature extraction, and feature selection. The selection methods used include principal components analysis (PCA) and mutual information (MI), which are used to determine the relevance and redundancy of extracted features and are performed in various combinations. Finally, because there is an inherent bias toward certain road and carriageway labelings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height

Application of ¹⁴C analyses to source apportionment of carbonaceous PM_2.5 in the UK

Determination of the radiocarbon (&lt;sup&gt;14&lt;/sup&gt;C) content of airborne particulate matter yields insight into the proportion of the carbonaceous material derived from fossil and contemporary carbon sources. Daily samples of PM&lt;sub&gt;2.5&lt;/sub&gt; were collected by high-volume sampler at an urban background site in Birmingham, UK, and the fraction of &lt;sup&gt;14&lt;/sup&gt;C in both the total carbon, and in the organic and elemental carbon fractions, determined by two-stage combustion to CO&lt;sub&gt;2&lt;/sub&gt;, graphitisation and quantification by accelerator mass spectrometry. OC and EC content was also determined by Sunset Analyzer. The mean fraction contemporary TC in the PM&lt;sub&gt;2.5&lt;/sub&gt; samples was 0.50 (range 0.27–0.66, n = 26). There was no seasonality to the data, but there was a positive trend between fraction contemporary TC and magnitude of SOC/TC ratio and for the high values of these two parameters to be associated with air-mass back trajectories arriving in Birmingham from over land. Using a five-compartment mass balance model on fraction contemporary carbon in OC and EC, the following average source apportionment for the TC in these PM&lt;sub&gt;2.5&lt;/sub&gt; samples was derived: 27% fossil EC; 20% fossil OC; 2% biomass EC; 10% biomass OC; and 41% biogenic OC. The latter category will comprise, in addition to BVOC-derived SOC, other non-combustion contemporary carbon sources such as biological particles, vegetative detritus, humic material and tyre wear. The proportion of total PM&lt;sub&gt;2.5&lt;/sub&gt; at this location estimated to derive from BVOC-derived secondary organic aerosol was 9–29%. The findings from this work are consistent with those from elsewhere in Europe and support the conclusion of a significant and ubiquitous contribution from non-fossil biogenic sources to the carbon in terrestrial aerosol

Characterization of Polar Organic Components in Fine Aerosols in the Southeastern United States: Identity, Origin, and Evolution

Filter samples of fine aerosols collected in the Southeastern United States in June 2004 were analyzed for the characterization of polar organic components. Four analytical techniques, liquid chromatography –mass spectrometry, ion trap mass spectrometry, laser desorption ionization mass spectrometry, and high-resolution mass spectrometry, were used for identification and quantification. Forty distinct species were detected, comprising on average 7.2% and 1.1% of the total particulate organic mass at three inland sites and a coastal site, respectively. The relative abundance of these species displays a rather consistent distribution pattern in the inland region, whereas a different pattern is found at the coastal site. Chemical and correlation analyses suggest that the detected species are secondary in nature and originate from terpene oxidation, with possible participation of NOx and SO2. It is estimated that polar, acidic components in fine aerosols in the Southeastern United States cover a molecular weight range of 150–400 Da and do not appear to be oligomeric. Other components with MW up to 800 Da may also be present. The detected polar organic species are similar to humic-like substances (HULIS) commonly found in fine aerosols in other rural areas. We present the first, direct evidence that atmospheric processing of biogenic emissions can lead to the formation of certain HULIS species in fine aerosols, and that this may be a typical pathway in the background atmosphere in continental regions; nevertheless, a natural source for HULIS, such as from aquatic and/or terrestrial humic/fulvic acids and their degradation products, cannot be precluded

Mobilisation of arsenic from bauxite residue (red mud) affected soils: effect of pH and redox conditions

The tailings dam breach at the Ajka alumina plant, western Hungary in 2010 introduced ~1 million m3 of red mud suspension into the surrounding area. Red mud (fine fraction bauxite residue) has a characteristically alkaline pH and contains several potentially toxic elements, including arsenic. Aerobic and anaerobic batch experiments were prepared using soils from near Ajka in order to investigate the effects of red mud addition on soil biogeochemistry and arsenic mobility in soil–water experiments representative of land affected by the red mud spill. XAS analysis showed that As was present in the red mud as As(V) in the form of arsenate. The remobilisation of red mud associated arsenate was highly pH dependent and the addition of phosphate to red mud suspensions greatly enhanced As release to solution. In aerobic batch experiments, where red mud was mixed with soils, As release to solution was highly dependent on pH. Carbonation of these alkaline solutions by dissolution of atmospheric CO2 reduced pH, which resulted in a decrease of aqueous As concentrations over time. However, this did not result in complete removal of aqueous As in any of the experiments. Carbonation did not occur in anaerobic experiments and pH remained high. Aqueous As concentrations initially increased in all the anaerobic red mud amended experiments, and then remained relatively constant as the systems became more reducing, both XANES and HPLC–ICP-MS showed that no As reduction processes occurred and that only As(V) species were present. These experiments show that there is the potential for increased As mobility in soil–water systems affected by red mud addition under both aerobic and anaerobic conditions