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The purpose of this paper is to describe a post‐processing procedure for defining load paths and a load bearing topology using the stresses from a finite element analysis.

Cauchy stress vectors and a Runge‐Kutta algorithm are used to identify the paths being followed by load components aligned with the coordinate axes. An algorithm is then defined which identifies an efficient topology that will carry the loads by straightening the paths.

The aim of the algorithm is to provide insight into the way a structure is carrying loads by identifying the material most effective in performing the load transfer. The procedure is applied to a number of structures to demonstrate its applicability to structural design.

The examples demonstrate an insight of structural behavior that is useful at the conceptual stage of the design process. The load paths identify load transfer and warn the designer of the creation of bending moments and the location of features such as holes on the load path. They also demonstrate that the new procedures can provide suggestions for alternate topologies for the load bearing structure.

The load path theory has been published elsewhere. The new work in this paper is the definition of the Runge‐Kutta algorithm to define the paths and the algorithm to identify the topology performing the load transfer.

November 10, 1966 Building — Safety regulations — “Working place” — Flat roof — Workman constructing flat concrete roof — No guard‐rails — Man's fall from roof — Whether roof “working place” — Building (Safety, Health and Welfare) Regulations, 1948 (S.I. 1948, No. 1145), reg.24(1).

Daring to challenge the status quo impacts innovation. Yet, successful outcomes depend on individual risk-taking and choice to influence others to support new ideas. This Challenging the Status Quo exercise illustrates how leaders use power and influencing tactics to challenge norms by analyzing Donald Trump’s journey as the 45^th U.S. President to defy experts and successfully influence followers to support his non-traditional candidacy: businessman lacking political experience becoming leader of the free world. Through integrating videoclips and polls, instructors make power visible, relevant, and thought-provoking as students apply power theory and influencing tactics perspectives to analyze (a) how leaders impact followers’ perceptions, (b) students mutual-influencing strategies, (c) power’s relationship with social identity and privilege, and (d) social impact on innovation via activism and free speech.

Current issues of Publishers' Weekly are reporting serious shortages of paper, binders board, cloth, and other essential book manufacturing materials. Let us assure you these shortages are very real and quite severe.

Two studies in the context of English‐French relations in Québec suggest that individuals who strongly identify with a group derive the individual‐level costs and benefits that drive expectancy‐value processes (rational decision‐making) from group‐level costs and benefits. In Study 1, high identifiers linked group‐ and individual‐level outcomes of conflict choices whereas low identifiers did not. Group‐level expectancy‐value processes, in Study 2, mediated the relationship between social identity and perceptions that collective action benefits the individual actor and between social identity and intentions to act. These findings suggest the rational underpinnings of identity‐driven political behavior, a relationship sometimes obscured in intergroup theory that focuses on cognitive processes of self‐stereotyping. But the results also challenge the view that individuals' cost‐benefit analyses are independent of identity processes. The findings suggest the importance of modeling the relationship of group and individual levels of expectancy‐value processes as both hierarchical and contingent on social identity processes.

An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science).

The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials.

The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches.

The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.