Coffee drinking has been associated with benefits for various health outcomes, with many attributed to the most prevalent family of polyphenols within coffee, chlorogenic acids (CGA). Whilst reviews of the association between coffee and cognition exist, evidence exploring effects of coffee-specific CGA on cognition has yet to be systematically synthesised. The purpose was to systematically review the current literature investigating the relationship between CGA from coffee and cognitive performance. A further objective was to undertake a meta-analysis of relevant randomised controlled trials (RCT). Observational and intervention studies were included if they considered coffee-based CGA consumption in human participants and applied a standardised measure of cognition. Furthermore, intervention studies were required to define the CGA content and include a control group/placebo. Studies were excluded if they examined CGA alone as an extract or supplement. A search of Scopus, PubMed, Web of Science, ScienceDirect and PsycINFO resulted in including twenty-three papers, six of which were interventions. The evidence from the broader systematic review suggests that CGA from coffee may need to be consumed chronically over a sustained period to produce cognitive benefits. However, the meta-analysis of RCT showed no benefits of coffee CGA intake on cognitive function (d = 0.00, 95% CI −0.05, 0.05). Overall, this review included a limited number of studies, the sample sizes were small, and a wide range of cognitive measures have been utilised. This indicates that further, good-quality interventions and RCT are required to systematically explore the conditions under which coffee CGA may provide benefits for cognitive outcomes.

While a growing number of refugees is in need of humanitarian protection, most states are reluctant to admit them. For more than two decades, scholars have thought to understand this intricate challenge of international governance through the prism of collective action theory and the concept of refugee protection as an international public good. However, the specific benefits that states gain from refugee protection and that are assumed to constitute the public good remain surprisingly vague and under-specified. In this Reflection, we make three contributions to address this issue. First, we take stock of the literature and assess the evolution of the collective action theory in asylum governance. Second, we identify and conceptualize legitimacy, security, reputation, and development as four types of benefits that states derive from refugee protection. Third, we discuss the limitations of the dominant rational-choice approach and contend that the nature of refugee protection in the international realm is the product of international and domestic politics based on the contestation of interests and norms. These insights result in a series of recommendations for future research of refugee protection as a collective action problem.

We present Iowa Electronic Markets (IEM) forecasts for the popular vote shares in the 2024 US presidential election. We discuss the differences between IEM forecasts and polls, the influence of the first presidential debate, the changes resulting from Biden dropping out of the race, and the degree of uncertainty implied by IEM forecasts. On September 29, the IEM forecast a 9-percentage-point Democratic popular vote margin according to a thinly traded vote-share market and an 85.7% chance the Democrat will receive more votes than the Republican in a thickly traded winner-takes-all market. Using a distribution derived from both markets, the forecasts are for a 6- to 7-percentage-point Democratic margin and 87.0% chance of winning. However, significant uncertainty remains.

The variability in ground manoeuvre occurrences for aircraft landing gear is intrinsically linked to the airport geometries served by aircraft in-service and consequently, the cyclic loads that landing gear carry are driven by the route network and characteristics of aircraft operators. Currently, assumptions must be made when deriving fatigue load spectra for aircraft landing gear, which may fail to capture the operator characteristics, potentially leading to design conservatism. This paper presents the enhanced characterisation of ground turning manoeuvres within the Automatic Dependent Surveillance-Broadcast (ADS-B) trajectories for six narrow-body aircraft across a full-service carrier (FSC) and a low-cost carrier (LCC) fleet. The methodology presented within this paper employs ADS-B latitude and longitude information to overcome limitations of previous approaches, increasing the rate of correct manoeuvre identification within ADS-B trajectories to 77% of flights from the 50% rate achieved previously. When characterising the ground manoeuvres across 3,000 flights, significant differences in manoeuvre occurrences were observed between individual aircraft within the LCC fleet and between the FSC and LCC fleets. The occurrence of tight and pivot turns were shown to vary across the six aircraft with six and eight fatigue-critical turns being performed by the FSC and LCC fleet for every 10 flights performed. In addition, it was observed that the direction of fatigue critical turns is biased in specific directions, suggesting that individual main landing gear assemblies will accumulate fatigue damage at an increased rate, leading to greater justification for operator-specific spectra and structural health monitoring of aircraft landing gear.

How does the general public perceive immigrants, whom do they think of when thinking about “immigrants,” and to what extent are these perceptions related to the actual composition of immigrant populations? We use three representative online surveys in the United States, South Africa, and Switzerland (total N = 2,778) to extend existing work on the perception of immigrants in terms of geographic coverage and individual characteristics. We also relate these responses to official statistics on immigration and integration patterns. In all three countries, there are significant discrepancies between perceptions of immigrants and their real proportion in the population and characteristics. Although we observe clear country differences, there is a striking pattern of people associating “immigrants” with “asylum seekers” in all three countries. We consider two possible explanations for the differences between perceptions and facts: the representativeness heuristic and the affect heuristic. In contrast to previous research, we find only partial support for the representativeness heuristic, whereas the results are consistent with the affect heuristic. We conclude that images of “immigrants” are largely shaped by pre-existing attitudes.

This article considers both presidential approval and party brand differentials, as measured by the generic ballot, to forecast the 2024 US presidential and congressional elections. Although both variables are leveraged to forecast collective partisan election outcomes, we consider the variables together as distinct determinants of partisan fortunes at both the executive and legislative levels. First, using a novel time series of mass national opinion since 1937, we show that presidential approval and generic brands are distinct conceptual and empirical measures of mass public assessments of collective institutions. Second, in a series of fully specified models validated with out-of-sample predictions, we show that presidential approval is the main predictor of presidential elections, yet, perhaps surprisingly, the vast bulk of the incumbent party’s performance in congressional elections is explained by partisan brands. Lastly, we forecast the 2024 U.S. national elections and find that Republicans are well positioned to win back the White House this November. By contrast, our model forecasts control of both chambers of the US Congress to be essentially a tied contest.

The Partisan-Bounded Economic Model forecasts popular vote and Electoral College vote results in the 2024 presidential election based on economic growth, presidential popularity, and shifts in party identification within the electorate. Due to the partisan vote and voter inertia, presidential elections are unlikely to result in lopsided outcomes even in the face of economic turmoil or prosperity. In the Partisan-Bounded Economic Model, outliers in the annualized GDP growth are adjusted to fall within a fixed range of 5 percent to -5 percent. Any GDP growth values exceeding 5 percent or falling below -5 percent are re-coded as 5 percent and -5 percent, respectively. The model predicts that Vice President Kamala Harris will secure 52.3 percent of the party vote, 49.4 percent of the total popular vote, and 59.1 percent of the Electoral College votes.

Our 2020 analysis correctly forecasted Joe Biden’s victory and the outcome of every state except Georgia. That forecast relied on economic data from 125 days prior to Election Day and presidential approval data from 104 days (or more) before the election. Since 2000, our model would have correctly forecasted the winner in 95% of all states. We updated our State Presidential Approval/State Economy Model for 2024. This article summarizes the model and its historical accuracy as well as new data updates. We then generate forecasts for the overall two-party popular vote, each state’s outcome, and the Electoral College winner for the 2024 US presidential election. One hundred days prior to Election Day, our model forecasts a split two-party popular vote (50.3% for Trump, 49.7% for Harris) but a notable Trump advantage in the Electoral College, with slightly less than a three-in-four chance that Trump wins the election. This Republican advantage 100 days prior to Election Day sheds light on Biden’s abrupt decision to drop out of the race and suggests that if Harris wins, she will have overcome extremely challenging fundamentals, and/or that Donald Trump and the Republican Party will have squandered a sizeable Electoral College advantage.

This paper analyzes the Jin Yong novel The Deer and the Cauldron through the lens of Etienne Balibar's theory of super-nationalism and supranationalism. The novel employs a pan-Asian racial ideology to expand national identity from Han Chinese to other ethnic groups (supranationalism) by introducing a racial Other, white Europeans, to unify warring groups. Simultaneously, Han culture is consistently uplifted as superior (super-nationalism). A critical sequence features the Kangxi Emperor asserting his legitimacy as the ruler of China to the protagonist Wei Xiaobao by claiming the Mandate of Heaven has passed from the Ming to the Qing dynasty. However, Han Chinese gallants and intellectuals constantly challenge his legitimacy because, as a Manchu, he is considered foreign. To resolve this issue, Wei Xiaobao begins constructing a racial national framework that includes Manchus. This paper further argues that Wei Xiaobao's moral relativism, unusual for a protagonist in martial arts fiction, enables the flexibility to redefine Chinese identity on racial grounds instead of moral or cultural. The Deer and the Cauldron illustrates the transition from the Mandate of Heaven to modern nation-state ideology in China, in the form of an irreverent martial arts fiction novel, crafted by the genre's greatest master.

Donald Trump’s bid for the 2024 Republican presidential nomination is unique in that no former president since Theodore Roosevelt in 1912 has sought the nomination of their political party, nor has a candidate sought the nomination while facing multiple criminal indictments. With data from previous nomination cycles, we use presidential nominations from 1980 to 2020 to create a forecast for the 2024 Republican primaries. The variables in the equations consist of data from the pre-primary period (e.g., money raised, cash reserves, elite endorsements, and polling results) and a second model with results of the Iowa caucuses and the New Hampshire primary to forecast the remaining primary vote. The models accurately predict Trump’s victory despite the unique nature of his candidacy.

With the upsurge of anti-globalizing ideologies and politics, the increasing institutionalization of xenophobia within the legal system has emerged as a pressing concern. Existing law and social science research has underexplored xenophobic bias in the US legal system. This article conceptualizes xenophobic bias as consisting of racism and nationalism. It investigates whether mock jurors reach different verdicts on defendant companies from foreign countries of origin (Japan, France, and China) compared to domestic (US) companies. Using a test simulating a patent lawsuit, the research finds no evidence of general xenophobic bias in juror liability verdict decisions, yet there is a specific bias against the Chinese company when granting damage awards. The similarity-leniency effect that has been established in the previous literature is corroborated in this article. Additionally, political views moderate the effects of the company’s country of origin on juror decisions. This research offers a more nuanced conceptual framework of xenophobic bias in juror decision-making for future law and social science research and informs judicial policies seeking to improve jury instructions and jury selection to reduce xenophobic bias.

The initial predictions presented in this article confirm that presidential candidate vote-share estimates based on AI polling are broadly exchangeable with those of other polling organizations. We present our first two biweekly vote-share estimates for the 2024 US presidential election and benchmark them against those being generated by other polling organizations. Our post–Democratic National Convention top-line estimates for Trump (47%) and Harris (46%) closely track measurements generated by other polls during the month of August. The subsequent early September (post-debate) PoSSUM vote-share estimates for Trump (47%) and Harris (48%) again closely track with other national polling being conducted in the United States. An ultimate test for the PoSSUM polling method will be the final preelection vote-share results that we publish before Election Day on November 5, 2024.

Our political economy model, as it has come to be called, has offered up forecasts of the American presidential election outcome since the early 1980s. The model, based on referendum theory, as measured by the job performance of the president and the economy (1948 to the present), yields a forecast from data available in the summer of the election year. We consider alternative specifications of this parsimonious model, examining the possible effects of other economic measures, Covid-19, and incumbency advantage on forecasting. The current point estimate of the core political economy model predicts the Democratic candidate will receive 48 percent of the two-party popular vote, which translates into a narrow Electoral College loss for the incumbent party. This point forecast, however, comes with a considerable amount of uncertainty. There is an 11-point spread around our point estimate, which effectively means we have a horserace on our hands, with both horses close to the finish line.

In this article, a low phase noise signal source to be used as local oscillator in pulse Doppler radio frequency (PDRF) sensor is proposed. Innovative design techniques for realization of the low phase noise frequency source using phase-locked loop (PLL) and dielectric resonator (DR) are presented. Qualitative investigations have been carried out on the effect of phase noise in PDRF sensor performance. An X-band vibration resistant PLL-based frequency source with phase noise better than −95 dBc at 1 kHz frequency offset has been designed here. It also presents the design of a 7.6 GHz low phase noise, vibration resistant DR oscillator. Systematic analysis of the key design aspects, their thermal-vibrational stability, and ease of integration with hybrid microwave integrated circuits have been disclosed. A prototype board is fabricated, assembled in a compact mechanical enclosure of dimension 55 × 55 × 15 mm3. Finally, developed module is experimentally validated under 7.6 g rms magnitude random vibration test in three axes and compared results with other state of-the-art similar works. The comparison clearly shows the merit of present research work over other similar existing works.

Despite governors’ crucial roles in shaping important policies, including abortion, education, and infrastructure, forecasters have paid little attention to gubernatorial elections. We posit that institutional idiosyncrasies and lack of public opinion data have exacerbated the classic problem facing all election forecasts: there are too many predictors and too few cases, leading to overfitting. To address these problems, we combine new governor and state-level presidential approval data with a machine-learning approach, LASSO, for variable selection. LASSO examines numerous variables but retains only those that substantively improve model performance. Results demonstrate the efficacy of gubernatorial and presidential approval ratings measured two quarters preelection in predicting both incumbent-party vote share and election winners in out-of-sample predictions. For 2022, our approach outperformed the Cook Political Report’s Partisan Voting Index and compared well with 538’s Election Day prediction. For 2024, our LASSO-Popularity model predictions indicate that it will likely be a difficult year for Democrats in gubernatorial contests.

From the Freedom Rides to the students’ lunch counter sit-ins, the campaigns of the civil rights movement are seen as the archetypes of civil disobedience. Pineda's wonderful and brilliant book draws on rich archival and historical research to peel the layers of idealization, romanticization, and ideology that have turned the classical phase of the civil rights movement (1954–1965) into both myth and protest template. This illuminating, insightful, and beautifully written book is a must-read for anyone interested in civil disobedience.

Latin American governments are increasingly adopting mano dura initiatives to combat gangs, organized crime, and insecurity. While mano dura has been a concept of increasing empirical interest, there seems to be limited conceptual clarity about the wide spectrum of strategies developed to combat crime and associated fear. This article proposes a definition of mano dura that has three different dimensions, each of them containing specific elements. The form of mano dura depends on formal, informal, and rhetorical practices. Drawing on 46 scholarly works in the social sciences, we develop our definition anchored in the knowledge of Latin American policing strategies, contributions on responses to crime in the region, and the conceptual development literature. With the purpose of supplementing our effort to standardize the usage of the term with the need to retain a degree of conceptual differentiation, we also offer a stylized model to better classify policing strategies in Latin America. In our stylized model, the numerous ways policies and narratives as well as their implementation (or not) interact can be grouped into four broad categories: full mano dura, institutional mano dura, performative mano dura, and covert mano dura.