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.

In a prospective, remote natural history study of 277 individuals with (60) and genetically at risk for (217) Parkinson’s disease (PD), we examined interest in the return of individual research results (IRRs) and compared characteristics of those who opted for versus against the return of IRRs. Most (n = 180, 65%) requested sharing of IRRs with either a primary care provider, neurologist, or themselves. Among individuals without PD, those who requested sharing of IRRs with a clinician reported more motor symptoms than those who did not request any sharing (mean (SD) 2.2 (4.0) versus 0.7 (1.5)). Participant interest in the return of IRRs is strong.

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.

Rice cultivation in Italy is usually performed by direct seeding in flooded or in dry fields. Mechanical rice transplanting is a technique that can help control weeds and improve rice competition. To test the feasibility of the technique for different rice varieties in Italy, a study was conducted in the Lombardy region (northwest Italy), from 2016 to 2018. The study also evaluated the efficacy of hoeing in transplanted rice fields. The experiment consisted of two studies, a ‘field plot experiment’ (conducted from 2017 to 2018) and an ‘on-farm transplanting trial’ (conducted from 2016 to 2018). The ‘field plot experiment’ was carried out using a split plot design to determine the optimal transplanting distances within the row (12 or 17 cm) and the most suitable rice varieties for transplanting. Hoeing was performed once in 2017 and twice in 2018. Weed infestation was assessed by counting the number of weeds within a randomly placed square frame in the interrow areas, both before and after hoeing. Rice plant density, panicle density, yield, and yield components were also assessed. The ‘on-farm transplanting trial’ tested transplanting in several farms over the years. Different rice varieties were transplanted using the same machines, and hoeing was performed according to a predetermined schedule. Transplanter performance was assessed as well as rice yield data in all fields, while four fields were selected each year to assess hoeing efficacy against weeds. ANOVAs were used to test the differences in weed control and rice parameters among varieties and transplanting distances. The ‘field plot experiment’ showed that the transplanting distance did not affect weeds or rice variety. Transplanting at 12 cm within the row resulted in a higher plant density compared to 17 cm, however tillering compensated for the difference in the number of panicles. Carnaroli consistently recorded the lowest yield, less than 2 t ha−1, while Selenio, Spillo, and Laser seemed to be better suited for transplanting achieving the highest yield in 2018 (about 7 ha−1). In the ‘on-farm transplanting experiment’ hoeing was effective in controlling weeds, although the machineries used were not always able to function properly in saturated soil. Most of the transplanted field yielded approximately 3 to 5 t ha−1. Varieties with round grain exhibited the greatest yield variability among fields. The study suggests that to achieve a high yield in organic rice, the transplanting technique should be combined with an effective interrow tillage to control weeds.

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.

External advisory committees (EACs) are critical peer-review meetings that drive improvement at Clinical and Translational Science Award Program Hubs. Despite their ubiquity, evaluations of EAC optimization and effective implementation remain scarce. We present a two-tiered approach to optimizing EAC meetings through (1) in-depth, topically focused “pre-review” meetings comprised of external topic experts and at least one standing “full-board” EAC member, followed by (2) a traditional “full-board” EAC meeting. This approach allowed pre-review discussion of program-focused topics and specific recommendations, later delivered to the full-board for review and direction. To evaluate this approach, we interviewed 18 people who planned, administered, or attended pre-review and/or full-board meetings, including internal Hub staff, external topic experts, and standing EAC members. Thematic analysis was used to explore planning, implementation, and value of our two-tiered approach versus the traditional single full-board approach. Interviewees preferred the two-tiered approach, noting benefits including additional time to reflect, shared identification of strengths and challenges, and discussion of solutions to share later with the full-board. Those who attended pre-review meetings described building “transformational,” rather than “transactional,” relationships with invitees through more discussion and inter-hub sharing. That increased sharing invited more exploration, discussion, and planning of next steps toward innovation.

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.

The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. Machine learning has previously been used to reliably “screen” articles for review – that is, identify relevant articles based on reviewers’ inclusion criteria. The application of machine learning techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We, therefore, set out to develop a series of tools that would assist in the profiling and analysis of 1952 publications on the theme of “outcomes-based contracting.” Tools were developed for the following tasks: assigning publications into “policy area” categories; identifying and extracting key information for evidence mapping, such as organizations, laws, and geographical information; connecting the evidence base to an existing dataset on the same topic; and identifying subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of machine learning techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Beyond this, our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While machine learning techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analyzing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases.

This study aimed to investigate the factors contributing to the gap in the use of hygienic materials during menstruation to collect blood among adolescent women between Aspirational and the remaining districts of India. The study sample consisted of 114805 adolescent women (20835 women from 112 Aspirational districts and 93970 women from 595 remaining districts) from the National Family Health Survey-5. Fairlie decomposition was used to identify and measure the factors contributing to the gap in the use of hygienic materials between Aspirational and the remaining districts of India. This study determined that the use of hygienic materials during menstruation varied significantly between Aspirational and the remaining districts. While only 37% of adolescent women used hygienic materials in Aspirational districts, almost 52% did so in the remaining districts. Seventy-five per cent of Aspirational districts (84 of 112 districts) reported less than 50% use of hygienic materials, which is lower than the national average and the average of the remaining districts. It was revealed that nearly 90% of the total explained gap between the two groups was accounted for by household wealth, place of residence, exposure to mass media, and education level. Wealth was the main contributor to the gap, explaining about 46% of the difference in hygienic materials use between Aspirational and the remaining districts, followed by the place of residence (18%), exposure to mass media (15%), and education level (11%). Findings suggest that targeted interventions to improve access to hygienic materials among adolescent women in Aspirational districts, particularly those in the northern states of Uttar Pradesh, Bihar, and Chhattisgarh, are necessary. Policy efforts should focus on women from poor households, improving access to education, and expanding mass media exposure in Aspirational districts to reduce the gap in menstrual hygiene practices among adolescent women in India.

Lon Fuller’s fictional Purple Shirt regime, Victor Orbán’s illiberal democracy in today’s Hungary and the Kaczynski brothers’ recently ended unconstitutional republic of Poland are three examples of a ‘broken democratic polity’ in which many aspects of the rule of law and constitutional democracy have been compromised and cannot be fixed without a qualified majority, even if democratic forces come to power. In this article, I address the question of whether illegal state action of a democratic government is an appropriate means of restoring legitimacy to what I call a ‘broken’ polity. Put differently: Is it morally defensible for a new democratically elected government to override and replace the rules of illiberal constitutional reform in violation of formal legality? If so, under what conditions? I argue that a positive answer to this question is justified if we adopt a neo-republican approach to politics and legitimacy.

Thrombocytopenia is a common symptom and one of the warning signs of dengue virus (DENV) infection. Platelet depletion is critical as it may lead to other severe dengue symptoms. Understanding the molecular events of this condition during dengue infection is challenging because of the multifaceted factors involved in DENV infection and the dynamics of the disease progression. Platelet levels depend on the balance between platelet production and platelet consumption or clearance. Megakaryopoiesis and thrombopoiesis, two interdependent processes in platelet production, are hampered during dengue infection. Conversely, platelet elimination via platelet activation, apoptosis and clearance processes are elevated. Together, these anomalies contribute to thrombocytopenia in dengue patients. Targeting the molecular events of dengue-mediated thrombocytopenia shows great potential but still requires further investigation. Nonetheless, the application of new knowledge in this field, such as immature platelet fraction analysis, may facilitate physicians in monitoring the progression of the disease.