In the first part of this paper I draw on some reflections offered by Descartes and Malebranche on the dangers of anthropomorphic conceptions of God, in order to suggest that there is something misguided about the way in which the so-called problem of evil is commonly framed. In the second part, I ask whether the problem of evil becomes easier to deal with if we adopt a non-personalist account of God, of the kind found in Aquinas. I consider the sense in which God is termed ‘good’ on this latter conception, and while not proposing that it can justify or explain the evil and suffering in the world, I suggest that the world’s manifest imperfections are compatible with the existence of a loving creator who is the source of the existence of the world and of the goodness found in created things.

Accurate dynamic model is essential for the model-based control of robotic systems. However, on the one hand, the nonlinearity of the friction is seldom treated in robot dynamics. On the other hand, few of the previous studies reasonably balance the calculation time-consuming and the quality for the excitation trajectory optimization. To address these challenges, this article gives a Lie-theory-based dynamic modeling scheme of multi-degree-of-freedom (DoF) serial robots involving nonlinear friction and excitation trajectory optimization. First, we introduce two coefficients to describe the Stribeck characteristics of Coulomb and static friction and consider the dependency of friction on load torque, so as to propose an improved Stribeck friction model. Whereafter, the improved friction model is simplified in a no-load scenario, a novel nonlinear dynamic model is linearized to capture the features of viscous friction across the entire velocity range. Additionally, a new optimization algorithm of excitation trajectories is presented considering the benefits of three different optimization criteria to design the optimal excitation trajectory. On the basis of the above, we retrieve a feasible dynamic parameter set of serial robots through the hybrid least square algorithm. Finally, our research is supported by simulation and experimental analyses of different combinations on the seven-DoF Franka Emika robot. The results show that the proposed friction has better accuracy performance, and the modified optimization algorithm can reduce the overall time required for the optimization process while maintaining the quality of the identification results.

This paper introduces a lower limb exoskeleton for gait rehabilitation, which has been designed to be adjustable to a wide range of patients by incorporating an extension mechanism and series elastic actuators (SEAs). This configuration adapts better to the user’s anatomy and the natural movements of the user’s joints. However, the inclusion of SEAs increases actuator mass and size, while also introducing nonlinearities and changes in the dynamic response of the exoskeletons. To address the challenges related to the human–exoskeleton dynamic interaction, a nonsingular terminal sliding mode control that integrates an adaptive parameter adjustment strategy is proposed, offering a practical solution for trajectory tracking with uncertain exoskeleton dynamics. Simulation results demonstrate the algorithm’s ability to estimate unknown parameters. Experimental tests analyze the performance of the controller against uncertainties and external disturbances.

Described in the Chinese Communist Party's orthodox historiography as a dark and repressive period and part of the “century of humiliation,” the Republican era has in recent decades undergone a significant reassessment in the People's Republic of China (PRC). In books, newspaper articles, documentaries and dramas, Republican China has sometimes been portrayed as a vibrant society making remarkable progress in modernization in the face of severe external challenges. This article explores the origins of this surprising rehabilitation and examines in detail how the Republican-era economic legacies have been reassessed in the reform era. It finds that while the post-Mao regime continues to use the negative view of China's pre-communist history to maintain its historical legitimacy, it has also been promoting a positive view of aspects of the same period in order to support its post-1978 priorities of modernization and nationalism, a trend that has persisted under Xi Jinping despite his tightened ideological control. The selective revival of Republican legacies, although conducive to the Party's current political objectives, has given rise to revisionist narratives that damage the hegemony of its orthodox historical discourses, on which its legitimacy still relies.

Originally founded in 2004 to improve election forecasting accuracy through evidence-based methods, the PollyVote project applies the principle of combining forecasts to predict the outcome of US presidential elections. The 2024 forecast uses the same methodology as in previous elections by combining forecasts from four methods: polls, expectations, models, and naive forecasts. By averaging within and across these methods, PollyVote predicts a close race, giving Kamala Harris a slight edge over Donald Trump in both the two-party popular vote (50.8 vs. 49.2%) and the Electoral College (276 vs. 262 votes). The forecast gives Harris a 65% chance of winning the popular vote and a 56% chance of winning the Electoral College, making both outcomes toss-ups. Compared to the combined PollyVote, component forecasts that rely on trial-heat polls tend to favor Harris, whereas methods that rely on alternative measures are less optimistic about the Democratic candidate’s chances. The polls may be overestimating Harris’s lead.

Voter turnout is a crucial indicator of democratic health, yet forecasting turnout remains an understudied area in political science. This article presents two pioneering models for predicting US presidential election turnout: the national and the state model. The national one, using data from 1868 from 2020, employs lagged turnout as its sole predictor. The state model, covering 1984 to 2020, incorporates demographic and institutional variables to forecast state-level participation. The national predicts 65.3% turnout for 2024, whereas the state model forecasts increased turnout in 41 states compared to 2020. The models’ ability to generate early predictions offers valuable lead time for planning and resource allocation, which has implications for election administrators and political campaigns, as well as for the vibrancy of civic engagement in America.

Using a forecasting model based on economic pessimism and recognizing the difficulties of making such a forecast in such atypical times, the forecasting model predicts a narrow loss for the incumbent presidential party and a loss of 12 seats in the House of Representatives. Even with the unusual nature of politics in the United States over the past decade, this model does a good job of predicting election outcomes. The more pessimistic people are, the worse the incumbent party does in presidential and House elections. Moreover, the power of incumbency shows strongly.

This model generates projections of the national popular vote and Electoral College votes a year in advance of the U.S. Presidential Election, before each party’s nominees are known. It forecasts the Democratic two-party popular vote in each state and the District of Columbia. It uses four independent variables: national head-to-head polling data 13 months prior to the election, the states’ prior election result, a party-adjusted home state advantage dummy variable, and a party adjusted variable simply counting the number of consecutive terms the current incumbent party has occupied the White House. New to this year’s model is a polling average approach that encompasses all possible candidate matchups for whom data is available. This year’s forecast suggests a distinct possibility of an Electoral College misfire benefitting the Republicans.

Seeing Like an Activist is a profound—and profoundly political—book. Skillfully distilling complex historical evidence into a vivid narrative and advancing a compelling theoretical argument, Pineda unsettles conventional accounts of the civil rights movement to ask what we can learn about the nature and limits of civil disobedience “by reconsidering the example we already think we know so well” (3). One of the book's key contributions is its analysis of political practices that constituted the “short civil rights movement”—the decade of southern protest bookended by the Montgomery Bus Boycott in 1955–56 and the Selma March in 1965. “The civil rights movement (and its multitude of activists),” Pineda observes, “operates between the lines of theory as an object lesson—a ready-made example that proves the moral purchase of the theory, rather than a live source of novel theoretical insights and political claims” (48). As object lesson, the movement is often recruited to police or criticize forms of protest that are said to fall short of the disciplined nonviolence of the past. This view of history, Pineda contends, enables theorists of civil disobedience like John Rawls to use the example of civil rights activism to affirm the liberal constitutional order. Offering incisive critiques of Rawls, Michael Walzer, and Hugo Bedau, Pineda exposes the unearned ease with which political theories that do not take racial injustice as a central concern or focus on Black theoretical practices have conscripted the civil rights movement in service of their arguments.

Statesman and scholar Alexis de Tocqueville (1876) once noted, “History is a gallery of pictures in which there are few originals and many copies.” In other words, history has a habit of repeating itself, and we can deduce cycles and patterns that likely will recur. Such stability and inertia should bode well for prediction. Nevertheless, when it comes to election forecasting, especially in the United States, most prognostications rely on short-term political fundamentals measuring macroeconomic performance or government or leader popularity. In this contribution, we adopt a structural approach but depart from existing literature by focusing on historical party and governance dynamics in the vein of de Tocqueville to establish whether they offer solid guidance regarding the performance of Democrats in US congressional elections. Our ex-post political history models provide solid predictions of which party will control Congress and the Democrats’ seat tally in each chamber between 1946 and 2022. This creates conditions to assume that political history may help us forecast Campaign 2024. Our study applied this political history model to predict the 2024 congressional elections. It forecast Democrats to lose control of the Senate with a net loss of three seats and estimated an exceptionally close race for House control, with the point estimate for the House suggesting that the Democrats would fall short of winning control.

Election forecasting in modern democracies faces significant challenges, including increasing survey nonresponse and selection bias. Moreover, there are limitations to the current predictive approaches. Whereas structural models focus solely on macro-level variables (e.g., economic conditions and leader popularity), thereby overlooking the importance of individual-level factors, survey-based aggregation methods often rely on intuitive procedures that lack theoretical foundations. To address these gaps, this article proposes a combined (i.e., both standard and Bayesian) logistic regression approach that leverages voter-level data and incorporates a theory-based specification. By testing these models on recent waves of the American National Election Studies Time Series, this study demonstrates that the proposed approach yields notably accurate predictions of Republican popular support in each election.

Between fifteen and twenty-five million Americans took to the streets in the summer of 2020 to march, mourn, occupy highways, clash with police, and be together in grief and rage. Municipal and state police forces responded with a national campaign of excessive force. Demonstrators were clubbed, tear gassed, sprayed with chemical agents, kettled and trampled, illegally detained, and mutilated by “less-than-lethal” munitions. Internal police reviews and municipal leaders blamed the violence on insufficient training but the scale and intensity of repression suggest a more profound democratic crisis surrounding the criminalization of dissent.

Pineda's book is an original and imaginative reconstruction of activist strategy and political theory, upending the orthodox account of civil disobedience during the civil rights movement of the 1950s and 1960s. Against liberal and democratic theorists who domesticate civil disobedience in the United States, with the aim of legitimizing the nation's constitutional democracy as “nearly just,” or in need of reform rather than revolution, Pineda argues that civil rights activists “theorized and deployed” civil disobedience as “decolonizing praxis” (16). She traces the decades-long history in which Black activists in the United States crafted their strategy of civil disobedience through embodied spaces, relationships, and practices of “imaginative transit” with revolutionaries in India, South Africa, and Ghana. These networks of transit enabled activists to diagnose segregation and colonialism as sites of a “shared, global condition” (59) of white supremacy, “operating with a common, reinforcing logic” of economic, political, and psychological domination (89). As a decolonizing praxis, civil disobedience combined an “inward-facing politics” of self-emancipation with an “outward-facing politics” (16) of disruption and disclosure—aimed not at reforming aberrations in constitutional democracy, but at “transforming the psychological, structural, and relational bases of white supremacy” (59).

The additionality of forest conservation interventions is frequently questioned. In particular, they are often considered to be located in places where forests are not threatened, which points to the existence of location biases. Revisiting this location bias concept, we conceptually distinguish potential and effective additionality and theoretically consider how the objectives of the implementer affect the siting choice of the forest conservation interventions and their additionality. Our theoretical intuition is that the choices of the implementers are influenced by the quality of institutions. Our results show that (1) the implementer's objective and local institutions may lead the implementer to select a site with low development potential and low forest threat, and (2) the selection of a site with low development potential, which is frequently presented as a location bias, does not necessarily preclude additionality.