Globally, resettlement is considered one of the most durable solutions for refugees. The UK has introduced a Community Sponsorship Scheme that enables communities to resettle refugee families providing them with enhanced integration support aided by volunteers. This paper investigates the nature of integration support that sponsored refugees receive utilising the analytical framework of UK’s Indicators of Integration (IoI). Data was collected from interviews with refugee adults resettled in diverse and less diverse areas. Our findings illustrate the importance of support given by volunteer groups to enable access to resources and connections. We establish that there is much potential for sponsorship programmes to add value to refugee support suggesting that the current expansion of sponsorship from its Canadian roots may help facilitate refugee integration. However, further research is needed to uncover the long-term experiences of sponsored refugees and to compare their outcomes to those of forced migrants arriving via different mechanisms.

Harpetid and trinucleid trilobites share a similar and unusual morphology, the most striking feature of which is a wide, flattened cephalic brim with many pits or holes. This similarity was once interpreted as a sign that these two groups of trilobites were closely related, but in recent years it has instead been assumed that the ‘harpiform’ brim arose in both groups independently. However, relatedness and similarity can be difficult to disentangle in fossil taxa without close living relatives, and this assumption about the harpiform brim has never been explicitly tested. Our study re-evaluates the relationship between Harpetida and Trinucleioidea in order to test a longstanding assumption about trilobite relationships and as a case study in evaluating different kinds of morphological similarity in extinct groups. We inferred a new phylogenetic tree using parsimony methods and discrete morphological character data from a broad sampling of harpetids, trinucleids, and their relatives. Despite their gross morphological similarities, we found that harpetids and trinucleids were readily distinguished in our analyses, a result consistent with a hypothesis of multiple origins for the harpiform brim. By mapping brim-related characters across our new phylogeny, we identified a sequence of morphological innovations that arose in parallel in both groups and led ultimately in each case to the evolution of the harpiform brim. These results indicate that harpiform brims are a prime example of parallel evolution—the similar development of a morphological trait in distantly related taxa that nevertheless share a similar original morphology. In addition, our phylogeny supports the idea that trinucleids are specialized, harpiform asaphids, rather than an independent order of trilobites. We also provide new information on the relationships of the putative ‘basal-most’ members of Trinucleioidea, the Liostracinidae, and confirm recent assessments that this family is more distantly related to trinucleids.

Random bridges have gained significant attention in recent years due to their potential applications in various areas, particularly in information-based asset pricing models. This paper aims to explore the potential influence of the pinning point’s distribution on the memorylessness and stochastic dynamics of the bridge process. We introduce Lévy bridges with random length and random pinning points, and analyze their Markov property. Our study demonstrates that the Markov property of Lévy bridges depends on the nature of the distribution of their pinning points. The law of any random variables can be decomposed into singular continuous, discrete, and absolutely continuous parts with respect to the Lebesgue measure (Lebesgue’s decomposition theorem). We show that the Markov property holds when the pinning points’ law does not have an absolutely continuous part. Conversely, the Lévy bridge fails to exhibit Markovian behavior when the pinning point has an absolutely continuous part.

This Special Issue presents a wide array of election forecasting models for the 2024 US elections. Most of these models generate forecasts for the presidential, congressional, and gubernatorial races. The contributions are characterized by the variety of their approaches: citizen forecasting, electronic markets, large language models, machine learning, poll-based models, and regression analysis. This introduction first summarizes some of the lessons and challenges of election forecasting. We then provide a brief context of the 2024 campaign and a short overview of the articles included in the Special Issue. The forecasts point to a tight presidential race. The two-party popular-vote predictions are almost evenly split, with some favoring Donald Trump and others Kamala Harris. However, among the models that offer an Electoral College forecast, three predict that Harris will win and five predict that Trump will return to the presidency.

This study explores labour market segmentation within the Turkish construction industry, in a developing country context characterised by refugee influxes and heightened earthquake risks. We apply statistical and regression analyses using 2002–2020 Household Labour Force Survey data to explore segmentation with a specific focus on payment, job type and social security enrolment. The findings reveal a segmented labour market where the progress in regular, permanent and registered employment in the 2000s failed to encompass most construction workers. Lower wages, and temporary and unregistered work are more common among the youngest and oldest workers, those with poor education and qualification levels, immigrants, and those employed by micro enterprises. The construction industry lags behind both manufacturing and services in terms of registered and permanent employment rates and average wages. The prevalence of workers in elementary jobs with little education highlights the ongoing challenge of ensuring a highly skilled workforce, while reconstruction activities in earthquake-prone zones and the demand for urban transformation in Türkiye are increasing. We argue that improvements in working conditions constitute an urgent restructuring component in the sector for elevating the status of construction jobs, addressing the shortage of skilled labour and ensuring a high-quality building stock that upholds the right to a secure life in Türkiye.

Every four years, numerous election-forecasting models attempt to predict the results of the US presidential election. Regardless of the stability of any election system, such as the bipartisan system in the United States, conditions can arise (e.g., candidate resignations) that negatively impact forecasters’ ability to predict electoral outcomes. Citizen forecasting—that is, directly asking respondents who they think will win an election—has a long track record of successfully predicting presidential elections. This study proposes adapting a citizen forecasting measure originally intended for use in multiparty systems to predict the US presidential election in 2024. Using this measure, we created a forecast of the national-level popular vote and vote-share forecasts for seven swing states.

This study applies surveys of business and household expenditure to draw inferences about the size of regional multipliers to assess the cascading economic impacts of the data-limited Indonesian tropical tuna fishery. The average business-level production multiplier was estimated at around 1.3 across survey respondents, while household-level consumption effects were considerably higher, with the total economic effect roughly three times larger than the production value. A statistical analysis using generalized additive models suggests that there is considerable difference in production multipliers across regions, driven by the individual characteristics of operators, such as revenue/profit, size of the boat, type of gear, and the class of the port where the business is located. This research has the potential to provide a practical management tool to measure flow-on economic impacts of a fishery when information necessary for more formal economic analysis is unavailable, such as for data-limited fisheries or small regional studies.

Twitter has been a prominent forum for academics communicating online, both among themselves and with policy makers and the broader public. Elon Musk’s takeover of the company brought sweeping changes to many aspects of the platform, including public access to its data; Twitter’s approach to censorship and mis/disinformation; and tweaks to the affordances of the platform. This article addresses a narrower empirical question: What did Elon Musk’s takeover of the platform mean for this academic ecosystem? Using a snowball sample of more than 15,700 academic accounts from the fields of economics, political science, sociology, and psychology, we show that academics in these fields reduced their “engagement” with the platform, measured by either the number of active accounts (i.e., those registering any behavior on a given day) or the number of tweets written (including original tweets, replies, retweets, and quote tweets). We further tested whether this decrease in engagement differed by account type; we found that verified users were significantly more likely to reduce their production of content (i.e., writing new tweets and quoting others’ tweets) but not their engagement with the platform writ large (i.e., retweeting and replying to others’ content).

The outcome of the 2016 election made it abundantly clear that victory in US presidential contests depends on the Electoral College much more than on direct universal suffrage. This fact points to the importance of using state-level models to arrive at adequate predictions of winners and losers in US presidential elections. In fact, the use of a model disaggregated to the state level and focusing on three types of measures—namely, changes in the unemployment rate, presidential popularity, and indicators of long-term patterns in the regional strength of the Democratic and Republican parties—has in the past enabled us to produce fairly accurate forecasts of the number of Electoral College votes for the presidential candidates of the two major American parties. In this article, we bring various modifications to this model to improve its overall accuracy. With Joe Biden out of the race, this revised model predicts that Donald Trump will succeed in winning back the presidency with 341 electoral votes against 197 for Kamala Harris.

I provide a new analytical framework to understand the effectiveness of corruption prosecutions in the so-called Mani Pulite (Clean Hands) operation by showing how this operation was rooted in a populist interpretation of criminal rules and criminal procedure. The Clean Hands operation represented a successful breakthrough against the vast and complex corruption system that had sustained Italian politics for decades. I show that prosecutors in the Clean Hands operation interpreted legal rules through the lens of a deep-seated hostility between a vague conglomerate of “corrupt elites” and “virtuous citizens.” This populist interpretation of criminal and procedural rules introduced significant legal innovations that empowered judicial actors against systemic corruption by creating unprecedented incentives for defendants to cooperate with legal authorities. Consequently, the judicial professionals leading the Clean Hands operation also felt the need to shield themselves against retaliation for the use of these novel approaches to corruption prosecution by bringing their fight before the court of public opinion. To this end, the Clean Hands prosecutors made use of targeted media interventions to rally public support around their investigation and protect their work from political interference.

Advance consent could address many of the limitations traditional consenting methods pose to participation in acute stroke trials. We conducted a series of five focus groups with people with lived experience of stroke. Using an inductive thematic approach, two themes were developed: factors in favour of, and against, advance consent. Participants supported the idea of advance consent and highlighted trust, transparent communication and sufficient time as major factors that would positively affect their decision to provide advance consent. The results will be used to finalise a model of advance consent suitable for testing the feasibility in stroke prevention clinics.

In this article, we build a model to predict the state-level results of the 2024 election. We do so by using both polling from similar points in past election cycles and the results of the previous election. Notably, we update our model over time, and the coefficients of the two variables change as a result: the model puts more weight on polling as the election gets closer. As of September 1, 2024, we find that Kamala Harris is a narrow favorite to win the 2024 election, with a 57% chance of doing so. Currently, the model predicts she will win 289 electoral votes to Trump’s 249. However, there remains significant uncertainty, and the model will continue to be updated as the election nears.

In recent decades, there has been a global growth of the use of contract labour in the mining industry, primarily driven by cost/flexibility considerations. At the same time, contracting has been associated with poorer occupational health and safety (OHS) outcomes across a range of industries. Drawing on published research, theses, and government reports, this paper critically reviews the available evidence on the OHS effects of contract labour in mining and the likely implications of further growth in this trend. This evidence confirms that the use of contract labour is associated with worse OHS outcomes, and that the Ten Pathways and Pressure, Disorganisation, and Regulatory failure (PDR) models are both valuable in explaining this. The latter point is confirmed by a more detailed examination of four serious mine incidents in NSW and Queensland. The paper identifies some gaps and areas for further research as well as the actions that mining companies, regulators, and unions could take to improve contractor safety. Notwithstanding the latter, the paper argues that the most effective way of improving contractor safety in mines is reducing the use of contractors overall and concentrating their activities in areas such as major shutdowns/repairs, where contractors have specialised expertise to undertake non-routine tasks. Despite oft-repeated phrases such as zero-harm and management systems, the corporate shift to using contractors is primarily driven by cost-cutting and highlights how OHS is compromised by such priorities.

Drawing on the case of American Muslim voter engagement in the 2024 election season, this article argues that election-forecasting models – particularly state-based models – should integrate minority populations into their analysis as crucial variables. This is of particular significance in swing states. By including minority-voter engagement and related variables relevant to them such as pressing policy concerns (e.g., anti-war sentiment and racial attitudes), forecasters can better understand and predict electoral outcomes and address the gaps identified in traditional forecasting approaches. The recommendations presented in this article help election forecasters prepare for unexpected changes, such as the American Muslim shift of support away from President Biden in the 2024 primary election season.

Smectite growth is of importance across various fields due to its abundance on the surface of both Earth and Mars. However, the impact of the crystallinity of initial materials on smectite growth processes remains poorly understood. In this study, the kinetic processes of smectite growth were examined via experimental synthesis of trioctahedral Mg-Ni saponites. Mg-Ni saponites were synthesized using mixed precursors, specifically end-member Mg-saponite and Ni-saponite, which exhibit different crystallinities. The crystal chemistry and morphology of samples were analyzed using X-ray diffraction, Fourier-transform infrared spectroscopy, and high-angle annular dark-field scanning transmission electron microscopy. The experimental results converge towards these main conclusions: (i) the formation of Mg-Ni saponite solid solutions are promoted when the precursors are small particles, whereas large-particle precursors limit their own dissolution and do not yield Mg-Ni saponite solid solutions under the experimental conditions; (ii) because Ni exhibits a greater stability within the saponite structure compared to Mg, the Mg-Ni-saponite solid solutions formed more easily from the mixture of Ni-saponite germs and well-crystallized Mg-saponite precursors than from the mixture of Mg-saponite germs and well-crystallized Ni-saponite precursors; (iii) the dissolution extent (DE) of precursor mixtures increases with longer synthesis time, higher synthesis temperature, and larger gap between synthesis temperature of precursors and of samples, and stabilizes once it reaches a certain value. Thus DE can be used to estimate the kinetics of Mg-Ni saponite crystallization from precursor mixtures. These results obtained from the experimental Mg-Ni saponite system are useful for predicting the evolution processes of smectite in natural systems.

This paper proposes a kinematic calibration method of a novel 5-degree-of-freedom double-driven parallel mechanism with the sub-closed loop on limbs. At first, considering the introduction of a sub-closed loop significantly increased the complexity and difficulty of kinematic error modeling, an equivalent transformation method is proposed for the limb with a sub-closed loop. Then kinematic error model of the parallel mechanism is established based on the closed-loop vector method and parasitic motion analysis, which is verified by virtual prototype technology. Because the full kinematic error model is generally redundant, error parameter identifiability analysis is carried out by QR decomposition of the identification Jacobian matrix, and the redundant parameters are removed. Additionally, the Sequence Forward Floating Search algorithm is utilized to optimize measurement configurations to reduce the influence of measurement noise. Finally, with a laser tracker as the measuring device, numerical simulations and experiments are implemented to verify the proposed kinematic calibration method. The experiment results show that average position and orientation errors are reduced from 2.778 mm and 1.115° to 0.263 mm and 0.176°, respectively, within the prescribed workspace.

Controlling the landing position of a spinning ball is difficult when using a table tennis robot. A complete physical model requires the factoring in of aerodynamic elements and object collisions, and inaccurate environmental coefficients would increase the landing position error. This study proposed a landing position control method based on a cascade neural network (CNN) that consists of forward and recurrent neural networks (RNNs). The forward NNs are used to estimate the velocity of the outgoing ball according to the velocity and acceleration of the incoming ball captured by cameras and the desired velocity of the outgoing ball. The RNN is employed to reverse-predict ball displacement based on the state of the incoming ball, desired landing point, and ball flight duration. The experiments verified that the method proposed in this study achieved control of differently spinning balls more effectively than the locally weighted regression (LWR)-based model did. The success rate of the CNN at two of six desired landing points was 25.9% and 32.9% higher, respectively, compared with use of the LWR-based model.