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Social media platforms are increasingly receiving attention as legitimate locations for civil society discourse and social movement mobilization. Initial work by Lovejoy and Saxton suggests NGOs use digital platforms such as Twitter to engage their constituencies through information dissemination, community building, and mobilization to action. Here, we explore the applicability of Lovejoy and Saxton’s communicative functions framework to resistance movement behavior by exploring two examples of digital engagement in political conflict. Through content analysis of tweets using hashtag indicators #BDS and #ICC4Israel collected during the spring of 2015, we affirm Lovejoy and Saxton’s findings that information dissemination is the most prevalent communication function for grassroots and institutionally grounded movements. Further, we find that informational tweets in our sample often provide information about grievances, and therefore propose an expansion of the framework to accommodate tweets that may be more common in resistance movements than in NGO communication. In addition to general findings about the communicative functions framework, the content analysis yielded several findings specific to the resistance movements studied. Notably, we find that #BDS and #ICC4Israel tweets are overwhelmingly nonviolent, and that sentiment is generally favorable across both hashtags, with the exception of tweets focusing on academic boycott, which were more ambiguous.

Modeling of material behavior by physically or microstructure-based models helps in understanding the relationships between its properties and microstructure. However, the majority of the numerical investigations on the prediction of the deformation behavior of AA2024 alloy are limited to the use of phenomenological or empirical constitutive models, which fail to take into account the actual microscopic-level mechanisms (i.e. crystallographic slip) causing plastic deformation. In order to achieve accurate predictions, the microstructure-based constitutive models involving the underlying physical deformation mechanisms are more reliable. Therefore, the aim of this work is to predict the mechanical response of AA2024-T3 alloy subjected to uniaxial tension at different strain rates, using a dislocation density-based crystal plasticity model in conjunction with computational homogenization.

A dislocation density-based crystal plasticity (CP) model along with computational homogenization is presented here for predicting the mechanical behavior of aluminium alloy AA2024-T3 under uniaxial tension at different strain rates. A representative volume element (RVE) containing 400 grains subjected to periodic boundary conditions has been used for simulations. The effect of mesh discretization on the mechanical response is investigated by considering different meshing resolutions for the RVE. Material parameters of the CP model have been calibrated by fitting the experimental data. Along with the CP model, Johnson–Cook (JC) model is also used for examining the stress-strain behavior of the alloy at various strain rates. Validation of the predictions of CP and JC models is done with the experimental results where the CP model has more accurately captured the deformation behavior of the aluminium alloy.

The CP model is able to predict the mechanical response of AA2024-T3 alloy over a wide range of strain rates with a single set of material parameters. Furthermore, it is observed that the inhomogeneity in stress-strain fields at the grain level is linked to both the orientation of the grains as well as their interactions with one another. The flow and hardening rule parameters influencing the stress-strain curve and capturing the strain rate dependency are also identified.

Computational homogenization-based CP modeling and simulation of deformation behavior of polycrystalline alloy AA2024-T3 alloy at various strain rates is not available in the literature. Therefore, the present computational homogenization-based CP model can be used for predicting the deformation behavior of AA2024-T3 alloy more accurately at both micro and macro scales, under different strain rates.

In a recent issue of the Municipal Journal there appeared a short but apparently inspired article on the subject of London Government, in which is foreshadowed another drastic and apparently imminent alteration of the system of local administration at present in operation in the Metropolis.

This study examines forecast accuracy associated with the forecast of 55 revenue data series of 18 local governments. The last 18 months (6 quarters; or 2 years) of the data are held-out for accuracy evaluation. Results show that forecast software, damped trend methods, and simple exponential smoothing methods perform best with monthly and quarterly data; and use of monthly or quarterly data is marginally better than annualized data. For monthly data, there is no advantage to converting dollar values to real dollars before forecasting and reconverting using a forecasted index. With annual data, naïve methods can outperform exponential smoothing methods for some types of data; and real dollar conversion generally outperforms nominal dollars. The study suggests benchmark forecast errors and recommends a process for selecting a forecast method.

The 24‐modulc EMIAC II analogue computer installation supplied to Sir W. G. Armstrong Whitworth Aircraft Ltd. in 1959 is to be supplemented by a further 24 modules—making 48 modules in all.

Retail buyers' decisions result in billions of dollars of merchandise being purchased and offered for sale by retailers around the world. At present, retail buyers do not appear to be adequately harnessing consumer input to improve their forecasts. The purpose of this paper is to address this issue by introducing a new approach involving both retail buyers' consensus forecasts and those from a sample of “ordinary” consumers.

The authors introduce a new approach to online forecasting that involves both retail buyers' consensus forecasts and those from a sample of “ordinary” consumers.

The results suggest an opportunity to create what are termed retail prediction markets that offer significant potential to improve the accuracy of buyers' forecasts.

The authors go beyond crowd sourcing technology and show how retail prediction markets may offer significant potential to improve the accuracy of retail buyers' forecasts.