2,973 research outputs found

    Using deep learning to understand and mitigate the qubit noise environment

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    Understanding the spectrum of noise acting on a qubit can yield valuable information about its environment, and crucially underpins the optimization of dynamical decoupling protocols that can mitigate such noise. However, extracting accurate noise spectra from typical time-dynamics measurements on qubits is intractable using standard methods. Here, we propose to address this challenge using deep learning algorithms, leveraging the remarkable progress made in the field of image recognition, natural language processing, and more recently, structured data. We demonstrate a neural network based methodology that allows for extraction of the noise spectrum associated with any qubit surrounded by an arbitrary bath, with significantly greater accuracy than the current methods of choice. The technique requires only a two-pulse echo decay curve as input data and can further be extended either for constructing customized optimal dynamical decoupling protocols or for obtaining critical qubit attributes such as its proximity to the sample surface. Our results can be applied to a wide range of qubit platforms, and provide a framework for improving qubit performance with applications not only in quantum computing and nanoscale sensing but also in material characterization techniques such as magnetic resonance.Comment: Accepted for publication, 15 pages, 10 figure

    Integrated control of vector-borne diseases of livestock--pyrethroids: panacea or poison?

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    Tick- and tsetse-borne diseases cost Africa approximately US$4-5 billion per year in livestock production-associated losses. The use of pyrethroid-treated cattle to control ticks and tsetse promises to be an increasingly important tool to counter this loss. However, uncontrolled use of this technology might lead to environmental damage, acaricide resistance in tick populations and a possible exacerbation of tick-borne diseases. Recent research to identify, quantify and to develop strategies to avoid these effects are highlighted

    Climate Variability and Change Impact on Crop Production: Evidence from Ghana

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    This paper explores the impact of climate variability and/or change on two major crop yields (cassava and maize) and cash crop (cocoa) in two districts in different agroecological zones - Atwima Mponua (Semi-Deciduous Forest Zone) and Ejura-Sekyeredumase (Transition Zone) of the Ashanti Region of Ghana. A comparative-case mixed-methods research design was adopted for the study, involving household survey questionnaires, focus group discussions (FGDs) and in-depth interviews with key informants to discuss farmers’ perceptions about changes in climate and impact on crop yields. Three hundred participants were involved in the study - 150 from each district. The study also used time series panel data approach to analyse the impact of climate variables (mean annual maximum and minimum temperatures; and total rainfall) on the three crops over the period 1992 - 2014.Farmers perceived changes in the weather patterns - mainly increasing temperature and erratic and low rainfall. Besides, farmers had observed invasion of weeds; and dryness of aquatic habitats (especially, during dry periods); and loss of major staples. The findings from the analysis of secondary data corroborate farmers’ perceptions about changes in climate and its negative impacts on cassava and maize yields for the past 20-30 years. However, qualitative feedback about impact of climate variables on cocoa yield conflicted with the findings of analysis of secondary data. The findings from this study can form a basis for policy makers to develop region specific adaptation policies to address climate change impacts on crops studied and extend it to other crops. Keywords: Climate variability and change; Vulnerability; Food crop; Cash crop. DOI: 10.7176/JEES/12-12-03 Publication date: December 31st 202
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