Swiss News

Das müssen Sie wissen: Russland: Die Botschafter beraten heute über das 19. Sanktionspaket des Blocks.; UN: Die EU-Staaten einigen sich vage auf ein Klimaziel für 2035; Merz x Sanchez: Amtsantritt in Madrid ein politischer Tango; Verteidigung: Brenner-Basistunnel ein Meilenstein für Verteidigungsmobilität.

Le président ukrainien prévoit que les contributions des membres de l’OTAN pour l’achat d’armes américaines pour l’Ukraine via le mécanisme PURL s’élèveront à environ 3,5 milliards de dollars (2,9 milliards d’euros) d’ici octobre.

The post Volodymyr Zelensky s’attend à recevoir 3,5 milliards de dollars d’ici octobre pour l’achat d’armes américaines appeared first on Euractiv FR.

Dans l'édition d'aujourd'hui : les ambassadeurs de l'UE se réunissent pour discuter du 19e paquet de sanctions contre la Russie, plusieurs milliers de manifestants ont protesté contre l'austérité en France, les États membres de l'UE s'accordent provisoirement sur un vague engagement climatique pour 2035.

The post Maroš Šefčovič contre le bloc de la nostalgie appeared first on Euractiv FR.

Première journée de mobilisation réussie pour les syndicats, alors que plusieurs centaines de milliers de personnes ont manifesté hier dans toute la France contre l’austérité budgétaire, accentuant la pression sur le nouveau Premier ministre, Sébastien Lecornu.

The post « Taxer les riches » : des centaines de milliers de personnes dans les rues contre l’austérité appeared first on Euractiv FR.

A selection of the week's best photos from across the African continent and beyond.

Fin août 2025, la Croatie a détruit des cargaisons de fruits albanais contaminés. L'épisode révèle les failles du secteur agricole albanais et, alors que Tirana vise l'UE, l'urgence de réformes en profondeur.

- Articles / Courrier des Balkans, Agriculture, Albanie, Questions européennes, Economie, Santé

Today, the Council approved the EU’s statement of intent on its post-2030 NDC, outlining its intention to submit an NDC ahead of COP30.

The Council adopted a decision on the position that the EU will take in the EU – Moldova Association Committee in Trade Configuration as regards increasing market access for some exports not yet liberalised.

Main agenda items, approximate timing, public sessions and press opportunities.

Defence investment: Council authorises negotiations with UK and Canada on their participation in SAFE.

Council amends existing regulations to address current and emerging challenges in the context of the mid-term review of EU cohesion policy.

Today, the Council approved the EU’s statement of intent on its post-2030 NDC, outlining its intention to submit an NDC ahead of COP30.

The Council adopted a decision on the position that the EU will take in the EU – Moldova Association Committee in Trade Configuration as regards increasing market access for some exports not yet liberalised.

Main agenda items, approximate timing, public sessions and press opportunities.

Today, the Council approved the EU’s statement of intent on its post-2030 NDC, outlining its intention to submit an NDC ahead of COP30.

The Council adopted a decision on the position that the EU will take in the EU – Moldova Association Committee in Trade Configuration as regards increasing market access for some exports not yet liberalised.

Main agenda items, approximate timing, public sessions and press opportunities.

Er will sie loswerden. Trumps Begehren, die Vorständin der US-Notenbank Fed, Lisa Cook, zu entlassen, wurde aber abgeschmettert. Nun wendet er sich an eine Etage höher.

Amidst different global food insecurity challenges, like the COVID-19 pandemic and economic turmoil, this article investigates the potential of machine learning (ML) to enhance food insecurity forecasting. So far, only few existing studies have used pre-shock training data to predict food insecurity and if they did, they have neither done this at the household-level nor systematically tested the performance and robustness of ML algorithms during the shock phase. To address this research gap, we use pre-COVID trained models to predict household-level food insecurity during the COVID-19 pandemic in Uganda and propose a new approach to evaluate the performance and robustness of ML models. The objective of this study is therefore to find high-performance and robust ML algorithms during a shock period, which is both methodologically innovative and practically relevant for food insecurity research. First, we find that ML can work well in a shock context when only pre-shock food security data are available. We can identify 80% of food-insecure households during the COVID-19 pandemic based on pre-shock trained models at the cost of falsely classifying around 40% of food-secure households as food insecure. Second, we show that the extreme gradient boosting algorithm, trained by balanced weighting, works best in terms of prediction quality. We also identify the most important predictors and find that demographic and asset features play a crucial role in predicting food insecurity. Last but not least, we also make a contribution by showing how different ML models should be evaluated in terms of their area under curve (AUC) value, the ability of the model to correctly classify positive and negative cases, and in terms of the change in AUC in different situations.

Amidst different global food insecurity challenges, like the COVID-19 pandemic and economic turmoil, this article investigates the potential of machine learning (ML) to enhance food insecurity forecasting. So far, only few existing studies have used pre-shock training data to predict food insecurity and if they did, they have neither done this at the household-level nor systematically tested the performance and robustness of ML algorithms during the shock phase. To address this research gap, we use pre-COVID trained models to predict household-level food insecurity during the COVID-19 pandemic in Uganda and propose a new approach to evaluate the performance and robustness of ML models. The objective of this study is therefore to find high-performance and robust ML algorithms during a shock period, which is both methodologically innovative and practically relevant for food insecurity research. First, we find that ML can work well in a shock context when only pre-shock food security data are available. We can identify 80% of food-insecure households during the COVID-19 pandemic based on pre-shock trained models at the cost of falsely classifying around 40% of food-secure households as food insecure. Second, we show that the extreme gradient boosting algorithm, trained by balanced weighting, works best in terms of prediction quality. We also identify the most important predictors and find that demographic and asset features play a crucial role in predicting food insecurity. Last but not least, we also make a contribution by showing how different ML models should be evaluated in terms of their area under curve (AUC) value, the ability of the model to correctly classify positive and negative cases, and in terms of the change in AUC in different situations.