Impacts of considering climate variability on investment decisions in Ethiopia:

"Extreme interannual variability of precipitation within Ethiopia is not uncommon, inducing droughts or floods and often creating serious repercussions on agricultural and non-agricultural commodities. An agro-economic model, including mean climate variables, was developed to assess irrigation and road construction investment strategies in comparison to a baseline scenario over a 12-year time horizon. The motivation for this work is to evaluate whether the inclusion of climate variability in the model has a significant effect on prospective investment strategies and the resulting country-wide economy. The mean climate model is transformed into a variable climate model by dynamically adding yearly climate-yield factors, which influence agricultural production levels and linkages to non-agricultural goods. Nine sets of variable climate data are processed by the new model to produce an ensemble of potential economic prediction indicators. Analysis of gross domestic product and poverty rate reveal a significant overestimation of the country's future welfare by the mean climate model method, in comparison to probability density functions created from the variable climate ensemble. The ensemble is further utilized to demonstrate risk assessment capabilities. The addition of climate variability to the agro-economic model provides a framework, including realistic ranges of economic values, from which Ethiopian planners may make strategic decisions." Authors' abstractClimate variability, Water, Droughts, Flooding, Irrigation Economic aspects, Road construction Economic aspects, Investments, Economic situation, Agro-economic model,

Reframing climate change as a public health issue: an exploratory study of public reactions

<p>Abstract</p> <p>Background</p> <p>Climate change is taking a toll on human health, and some leaders in the public health community have urged their colleagues to give voice to its health implications. Previous research has shown that Americans are only dimly aware of the health implications of climate change, yet the literature on issue framing suggests that providing a novel frame - such as human health - may be potentially useful in enhancing public engagement. We conducted an exploratory study in the United States of people's reactions to a public health-framed short essay on climate change.</p> <p>Methods</p> <p>U.S. adult respondents (n = 70), stratified by six previously identified audience segments, read the essay and were asked to highlight in green or pink any portions of the essay they found "especially clear and helpful" or alternatively "especially confusing or unhelpful." Two dependent measures were created: a composite sentence-specific score based on reactions to all 18 sentences in the essay; and respondents' general reactions to the essay that were coded for valence (positive, neutral, or negative). We tested the hypothesis that five of the six audience segments would respond positively to the essay on both dependent measures.</p> <p>Results</p> <p>There was clear evidence that two of the five segments responded positively to the public health essay, and mixed evidence that two other responded positively. There was limited evidence that the fifth segment responded positively. Post-hoc analysis showed that five of the six segments responded more positively to information about the health benefits associated with mitigation-related policy actions than to information about the health risks of climate change.</p> <p>Conclusions</p> <p>Presentations about climate change that encourage people to consider its human health relevance appear likely to provide many Americans with a useful and engaging new frame of reference. Information about the potential health benefits of specific mitigation-related policy actions appears to be particularly compelling. We believe that the public health community has an important perspective to share about climate change, a perspective that makes the problem more personally relevant, significant, and understandable to members of the public.</p

A passive imaging system for geometry measurement for the plasma arc welding process

Automatic and flexible geometry measurement of the weld pool surface can help better understand the complex welding processes and even provide feedback to better control this process. Most of existing imaging systems use an additional source of illumination to remove the light interference coming from the welding arc but it is usually costly. This paper introduces a novel low-cost optical-sensor-based monitoring system working under passive mode to monitor the wire + arc additive manufacture (WAAM) process, particularly for plasma arc welding. Initially, configurations and parameters of camera are investigated to achieve good visualisation of weld pool. A novel camera calibration methodology using the nozzle of a CNC machine is then proposed for this imaging system allowing estimation of the camera position with respect to the inspecting surface and its orientation in an easy-to-use approach. The verification tests show that the average error of the calibration is less than 1 pixel. As a case study, an image analysis routine is proposed to measure the width of the bead during the welding process. The results show that the proposed system is effective to measure the dimension of weld pool

Safety Consideration for Emerging Wireless Technologies-Evaluations of Temperature Rise in Eyes for RF Radiations up to 10 GHz

© 2018 IEEE. The study of temperature rise distribution in the human eye under plane electromagnetic wave exposure up to 10 GHz is presented in this paper. The effects of different frequencies and different blood perfusion rates of sclera to thermal calculations are investigated by finite difference method. The results reveal that the changes in the thermal parameter produce a maximum relative standard deviation of ~15% in the temperature rise in lens

Disrupted Ultradian activity rhythms and Differential expression of several clock genes in interleukin-6-Deficient Mice

The characteristics of the cycles of activity and rest stand out among the most intensively investigated aspects of circadian rhythmicity in humans and experimental animals. Alterations in the circadian patterns of activity and rest are strongly linked to cognitive and emotional dysfunctions in severe mental illnesses such as Alzheimer's disease (AD) and major depression (MDD). The proinflammatory cytokine interleukin 6 (IL-6) has been prominently associated with the pathogenesis of AD and MDD. However, the potential involvement of IL-6 in the modulation of the diurnal rhythms of activity and rest has not been investigated. Here, we set out to study the role of IL-6 in circadian rhythmicity through the characterization of patterns of behavioral locomotor activity in IL-6 knockout (IL-6 KO) mice and wild-type littermate controls. Deletion of IL-6 did not alter the length of the circadian period or the amount of locomotor activity under either light-entrained or free-running conditions. IL-6 KO mice also presented a normal phase shift in response to light exposure at night. However, the temporal architecture of the behavioral rhythmicity throughout the day, as characterized by the quantity of ultradian activity bouts, was significantly impaired under light-entrained and free-running conditions in IL-6 KO. Moreover, the assessment of clock gene expression in the hippocampus, a brain region involved in AD and depression, revealed altered levels of cry1, dec2, and rev-erb-beta in IL-6 KO mice. These data propose that IL-6 participates in the regulation of ultradian activity/rest rhythmicity and clock gene expression in the mammalian brain. Furthermore, we propose IL-6-dependent circadian misalignment as a common pathogenetic principle in some neurodegenerative and neuropsychiatric disorders

Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks

As a critical component of coherent X-ray diffraction imaging (CDI), phase retrieval has been extensively applied in X-ray structural science to recover the 3D morphological information inside measured particles. Despite meeting all the oversampling requirements of Sayre and Shannon, current phase retrieval approaches still have trouble achieving a unique inversion of experimental data in the presence of noise. Here, we propose to overcome this limitation by incorporating a 3D Machine Learning (ML) model combining (optional) supervised learning with transfer learning. The trained ML model can rapidly provide an immediate result with high accuracy which could benefit real-time experiments, and the predicted result can be further refined with transfer learning. More significantly, the proposed ML model can be used without any prior training to learn the missing phases of an image based on minimization of an appropriate ‘loss function’ alone. We demonstrate significantly improved performance with experimental Bragg CDI data over traditional iterative phase retrieval algorithms