Modeling a system'stemporal behaviour in reaction to external stimuli is a fundamentalprobleminmanyareas.

Using weighted pieces and magnetic boards, players of the hybrid sport must complete each move in one breath.

Urban air pollution is a major health crisis causing millions of premature deaths annually, underscoring the urgent need for accurate and scalable monitoring of air quality (AQ). While low-cost sensors (LCS) offer a scalable alternative to expensive reference-grade stations, their readings are affected by drift, calibration errors, and environmental interference. To address these challenges, we introduce Veli (Reference-free Variational Estimation via Latent Inference), an unsupervised Bayesian model that leverages variational inference to correct LCS readings without requiring co-location with reference stations, eliminating a major deployment barrier. Specifically, Veli constructs a disentangled representation of the LCS readings, effectively separating the true pollutant reading from the sensor noise. To build our model and address the lack of standardized benchmarks in AQ monitoring, we also introduce the Air Quality Sensor Data Repository (AQ-SDR). AQ-SDR is the largest AQ sensor benchmark to date, with readings from 23,737 LCS and reference stations across multiple regions. Veli demonstrates strong generalization across both in-distribution and out-of-distribution settings, effectively handling sensor drift and erratic sensor behavior. Code for model and dataset will be made public when this paper is published.

Who is Rob Jetten, tipped to become youngest Dutch prime minister? Rob Jetten's achievement in dragging his socially liberal D66 party from fifth place to the top of Dutch politics in less than two years has been extraordinary. But politically, all the stars were perfectly aligned for the 38-year-old to do so. The result of Wednesday's election is too close to call, with Jetten vying with anti-Islam populist Geert Wilders for the most seats in parliament. No other political leader commanded as much screen time during the campaign as Jetten and his smile and cheerful message resonated with voters, while his rivals sometimes struggled.

As the Netherlands gears up for a snap parliamentary election on October 29, less than halfway through parliament's usual four-year term following the collapse of the ruling coalition, the likelihood of another win for the country's far-right Party for Freedom (PVV) is mounting. An outright win is next to impossible. The Netherlands has always had a coalition government formed by a minimum of two parties due to its proportional representation electoral system, under which seats in parliament are awarded to parties in proportion to the number of votes they win. It then partnered with three other far-right parties - the Farmer-Citizen Movement (BBB), New Social Contract (NSC), and the People's Party for Freedom and Democracy (VVD) - to form a coalition government. But in June, PVV made a dramatic exit from the coalition government over a disagreement on immigration policy.

The Netherlands's data protection watchdog has cautioned citizens against consulting with artificial intelligence on how to vote, warning that popular chatbots provide a "highly distorted and polarised view" of politics. The Dutch Data Protection Authority said on Tuesday that an increasing number of voters were using AI to help decide who to vote for, despite the models offering "unreliable and clearly biased" advice. The research found that the chatbots more often recommended parties on the fringes of the political spectrum when asked to identify the three choices that best matched the policy preferences of 1,500 fictitious voter profiles. In more than half of cases, the AI models identified the hard-right Party for Freedom (PVV) or left-wing Green Left-Labour Party as the top choice, the watchdog said. Parties closer to the political middle ground - such as the right-leaning People's Party for Freedom and Democracy and the centre-left Democrats 66 - were recommended much less often, according to the watchdog.

Machine learning (ML) promises to revolutionize public health through improved surveillance, risk stratification, and resource allocation. However, without systematic attention to algorithmic bias, ML may inadvertently reinforce existing health disparities. We present a systematic literature review of algorithmic bias identification, discussion, and reporting in Dutch public health ML research from 2021 to 2025. To this end, we developed the Risk of Algorithmic Bias Assessment Tool (RABA T) by integrating elements from established frameworks (Cochrane Risk of Bias, PROBAST, Microsoft Responsible AI checklist) and applied it to 35 peer-reviewed studies. Our analysis reveals pervasive gaps: although data sampling and missing data practices are well documented, most studies omit explicit fairness framing, subgroup analyses, and transparent discussion of potential harms. In response, we introduce a four-stage fairness-oriented framework called ACAR (A wareness, Conceptualization, Application, Reporting), with guiding questions derived from our systematic literature review to help researchers address fairness across the ML lifecycle. We conclude with actionable recommendations for public health ML practitioners to consistently consider algorithmic bias and foster transparency, ensuring that algorithmic innovations advance health equity rather than undermine it.

Why has Dutch government taken control of China-owned chipmaker Nexperia? The Dutch government has intervened to take effective control of technology group Nexperia, which is owned by Chinese group Wingtech Technology. The decision comes amid a growing rift between China and the West over the development of technology such as computer chips and semiconductors, which are essential components for the manufacture of artificial intelligence (AI) technology. Here is more about what the Dutch government announced, why and what happens next. What has the Dutch government announced?

A timeline of genetic changes in millions of years of human evolution shows that variants linked to higher intelligence appeared most rapidly around 500,000 years ago, and were closely followed by mutations that made us more prone to mental illness. The findings suggest a "trade-off" in brain evolution between intelligence and psychiatric issues, says Ilan Libedinsky at the Center for Neurogenomics and Cognitive Research in Amsterdam, the Netherlands. Why did humans evolve big brains? "Mutations related to psychiatric disorders apparently involve part of the genome that also involves intelligence. So there's an overlap there," says Libedinsky. "[The advances in cognition] may have come at the price of making our brains more vulnerable to mental disorders."

Administrative registry data can be used to construct population-scale networks whose ties reflect shared social contexts between persons. With machine learning, such networks can be encoded into numerical representations -- embeddings -- that automatically capture individuals' position within the network. We created embeddings for all persons in the Dutch population from a population-scale network that represents five shared contexts: neighborhood, work, family, household, and school. To assess the informativeness of these embeddings, we used them to predict right-wing populist voting. Embeddings alone predicted right-wing populist voting above chance-level but performed worse than individual characteristics. Combining the best subset of embeddings with individual characteristics only slightly improved predictions. After transforming the embeddings to make their dimensions more sparse and orthogonal, we found that one embedding dimension was strongly associated with the outcome. Mapping this dimension back to the population network revealed differences in network structure related to right-wing populist voting between different school ties and achieved education levels. Our study contributes methodologically by demonstrating how population-scale network embeddings can be made interpretable, and substantively by linking structural network differences in education to right-wing populist voting.