VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes. Recent success in deep learning has shown how to learn complex shape distributions in a data-driven way from large scale 3D CAD Model collections and to utilize them for 3D processing on volumetric representations and thereby circumventing problems of topology and tessellation. Prior work has shown encouraging results on problems ranging from shape completion to recognition. We provide an analysis of such approaches and discover that training as well as the resulting representation are strongly and unnecessarily tied to the notion of object labels. Thus, we propose a full convolutional volumetric auto encoder that learns volumetric representation from noisy data by estimating the voxel occupancy grids. The proposed method outperforms prior work on challenging tasks like denoising and shape completion. We also show that the obtained deep embedding gives competitive performance when used for classification and promising results for shape interpolation

Measurements of Wind-driven Rain on Mid- and High-rise Buildings in three Canadian Regions

AbstractWind-driven rain (WDR) is an important boundary condition for the study of the hygrothermal behaviour and durability of building envelopes. Understanding the WDR characteristics is important for establishing designs that minimize moisture related issues. Three buildings located in three Canadian cities (Fredericton, Montreal and Vancouver) have been instrumented with equipment to quantify the WDR loads on building façades. This paper presents the experimental setup, spatial distribution of WDR on façades in terms of wall factors, and the comparison between measurements and calculated WDR using the standard ISO 15927. The preliminary results show that the ISO model overestimates the WDR amount most of the time

The cost of inadequate sleep among on-call workers in Australia: A workplace perspective

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. On-call or stand-by is becoming an increasingly prevalent form of work scheduling. However, on-call arrangements are typically utilised when workloads are low, for example at night, which can result in inadequate sleep. It is a matter of concern that on-call work is associated with an increased risk of workplace injury. This study sought to determine the economic cost of injury due to inadequate sleep in Australian on-call workers. The prevalence of inadequate sleep among on-call workers was determined using an online survey, and economic costs were estimated using a previously validated costing methodology. Two-thirds of the sample (66%) reported obtaining inadequate sleep on weekdays (work days) and over 80% reported inadequate sleep while on-call. The resulting cost of injury is estimated at $2.25 billion per year ($1.71–2.73 billion). This equates to $1222 per person per incident involving a short-term absence from work;$2.53 million per incident classified as full incapacity, and $1.78 million for each fatality. To the best of our knowledge this is the first study to quantify the economic cost of workplace injury due to inadequate sleep in on-call workers. Well-rested employees are critical to safe and productive workplace operations. Therefore, it is in the interest of both employers and governments to prioritise and invest far more into the management of inadequate sleep in industries which utilise on-call work arrangements

Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging

Automated collection of large scale plant phenotype datasets using high throughput imaging systems has the potential to alleviate current bottlenecks in data-driven plant breeding and crop improvement. In this study, we demonstrate the characterization of temporal dynamics of plant growth and water use, and leaf water content of two maize genotypes under two different water treatments. RGB (Red Green Blue) images are processed to estimate projected plant area, which are correlated with destructively measured plant shoot fresh weight (FW), dry weight (DW) and leaf area. Estimated plant FW and DW, along with pot weights, are used to derive daily plant water consumption and water use efficiency (WUE) of the individual plants. Hyperspectral images of plants are processed to extract plant leaf reflectance and correlate with leaf water content (LWC). Strong correlations are found between projected plant area and all three destructively measured plant parameters (R2 \u3e 0.95) at early growth stages. The correlations become weaker at later growth stages due to the large difference in plant structure between the two maize genotypes. Daily water consumption (or evapotranspiration) is largely determined by water treatment, whereas WUE (or biomass accumulation per unit of water used) is clearly determined by genotype, indicating a strong genetic control of WUE. LWC is successfully predicted with the hyperspectral images for both genotypes (R2 = 0.81 and 0.92). Hyperspectral imaging can be a very powerful tool to phenotype biochemical traits of the whole maize plants, complementing RGB for plant morphological trait analysis

Source of lead pollution, its influence on public health and the countermeasures

Lead is a well-known toxic heavy metal, which can have serious public health hazards at very low levels, especially for young children. This report summarized the background information on lead as well as its applications, pollution sources, poisoning pathways, biomarkers of exposure and effect, toxicities, poisoning mechanisms, preventive actions, decontamination strategies, and detoxification methods.

High throughput in vivo analysis of plant leaf chemical properties using hyperspectral imaging

The possibility of predicting plant leaf chemical properties using hyperspectral images was studied. Sixty maize and 60 soybean plants were used, and two experiments were conducted: one with water limitation and the second with nutrient limitation, with the purpose of creating wide ranges of these chemical properties in plant leaf tissues. A hyperspectral imaging system with a spectral range from 550 to 1700 nm was used to acquire plant images in a high throughput fashion (plants placed on an automated conveyor belt). Leaf chemical properties were measured in the laboratory. Partial least squares regression was implemented on spectral data to successfully model and predict water content, micronutrient, and macronutrient concentrations