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Register a new userWhat do adoption patterns of solar panels observed so far tell about governments incentive? insight from diffusion models
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The paper uses diffusion models to understand the main determinants of diffusion of solar photovoltaic panels (SPP) worldwide, focusing on the role of public incentives. We applied the generalized Bass model (GBM) to adoption data of 26 countries between 1992-2016. The SPP market appears as a frail and complicate one, lacking public media support. Even the major shocks in adoption curves, following state incentive implemented after 2006, failed to go beyond short-term effects and therefore were unable to provide sustained momentum to the market. This suggests that further barriers to adoption should be removed.
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The DNA molecule, apart from carrying the genetic information, plays a crucial role in a variety of biological processes and find applications in drug design, nanotechnology and nanoelectronics. The molecule undergoes significant structural transitions under the influence of forces due to physiological and non-physiological environments. Here, we summarize the insights gained from simulations and single-molecule experiments on the structural transitions and mechanics of DNA under force, as well as its elastic properties, in various environmental conditions, and discuss appealing future directions.
Deeply virtual Compton scattering (DVCS) and timelike Compton scattering (TCS) leading twist amplitudes are intimately related thanks to their analytic properties as a function of $Q^2$. We exploit this feature to use Compton form factors previously extracted from available DVCS data and derive data-driven predictions for TCS observables to be measured in near future experiments. Our results quantitatively illustrate the complementarity of DVCS and TCS experiments.
As the numbers of submissions to conferences grow quickly, the task of assessing the quality of academic papers automatically, convincingly, and with high accuracy attracts increasing attention. We argue that studying interpretable dimensions of these submissions could lead to scalable solutions. We extract a collection of writing features, and construct a suite of prediction tasks to assess the usefulness of these features in predicting citation counts and the publication of AI-related papers. Depending on the venues, the writing features can predict the conference vs. workshop appearance with F1 scores up to 60-90, sometimes even outperforming the content-based tf-idf features and RoBERTa. We show that the features describe writing style more than content. To further understand the results, we estimate the causal impact of the most indicative features. Our analysis on writing features provides a perspective to assessing and refining the writing of academic articles at scale.
We analyze observations from the Interface Region Imaging Spectrograph of the Mg II k line, the Mg II UV subordinate lines, and the O I 135.6 nm line to better understand the solar plage chromosphere. We also make comparisons with observations from the Swedish 1 m Solar Telescope of the H{alpha} line, the Ca II 8542 line, and Solar Dynamics Observatory/Atmospheric Imaging Assembly observations of the coronal 19.3 nm line. To understand the observed Mg II profiles, we compare these observations to the results of numerical experiments. The single-peaked or flat-topped Mg II k profiles found in plage imply a transition region at a high column mass and a hot and dense chromosphere of about 6500 K. This scenario is supported by the observed large-scale correlation between moss brightness and filled-in profiles with very little or absent self-reversal. The large wing width found in plage also implies a hot and dense chromosphere with a steep chromospheric temperature rise. The absence of emission in the Mg II subordinate lines constrain the chromospheric temperature and the height of the temperature rise while the width of the O I 135.6 nm line sets a limit to the non-thermal velocities to around 7 km/s.
Approximately half of the global population does not have access to the internet, even though digital connectivity can reduce poverty by revolutionizing economic development opportunities. Due to a lack of data, Mobile Network Operators and governments struggle to effectively determine if infrastructure investments are viable, especially in greenfield areas where demand is unknown. This leads to a lack of investment in network infrastructure, resulting in a phenomenon commonly referred to as the `digital divide`. In this paper we present a machine learning method that uses publicly available satellite imagery to predict telecoms demand metrics, including cell phone adoption and spending on mobile services, and apply the method to Malawi and Ethiopia. Our predictive machine learning approach consistently outperforms baseline models which use population density or nightlight luminosity, with an improvement in data variance prediction of at least 40%. The method is a starting point for developing more sophisticated predictive models of infrastructure demand using machine learning and publicly available satellite imagery. The evidence produced can help to better inform infrastructure investment and policy decisions.
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