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Au terme d'une élection, lundi 15 septembre 2025, à Riyad, Fouzi Lekjaa, président de la Fédération royale marocaine de football (FRMF) a été reconduit à la tête de l'Union arabe de football (UAFA).

Le Maroc renforce sa présence sur la scène internationale de football. Le président de la Fédération royale marocaine de football, Fouzi Lekjaa, a été réélu président de l'Union arabe de football. Cette réélection qui intervient à quelques semaines du coup d'envoi de la Coupe d'Afrique des nations (CAN) est très appréciée dans les cercles du football arabe.
La présence du président de la FRMF aux côtés de personnalités influentes telles que Hani Abou Rida de l'Égypte, Ahmed Yahya de la Mauritanie, et Moatasem Jaafar du Soudan, au sein du comité exécutif de l'UAFA, est perçue comme une reconnaissance politique et stratégique qui s'inscrit également dans une dynamique de diplomatie sportive offensive, dans laquelle le Maroc entend jouer un rôle de carrefour entre le monde arabe et l'Afrique.
Dans le cadre de la CAN 2025 qui se jouera du 21 décembre 2025 au 18 janvier 2026, le Royaume du Maroc a engagé un vaste programme d'investissement pour moderniser ses stades, notamment ceux de Rabat, Marrakech, Tanger, Fès, Agadir pour répondre aux standards internationaux. Outre la plus grande compétition de football sur le continent africain, le Royaume du Maroc s'apprête pour accueillir en 2027, la Coupe arabe féminine de football. Un autre évènement sportif de grande envergure qui témoigne de l'engagement du Royaume à promouvoir le football féminin.

F. A. A.

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.

Le président de la République, Abdelmadjid Tebboune, a présidé aujourd’hui une réunion du Haut Conseil de sécurité. En sa qualité de chef suprême des forces […]

L’article Tebboune préside une réunion du Haut Conseil de sécurité est apparu en premier sur .

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.

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.

Ohne US-Präsident Donald Trump verbrachte dessen Ehefrau Melania am Donnerstag einige Stunden mit Prinzessin Kate. Die beiden Frauen wirkten gelöst und entspannt, scherzten viel und Melania lächelte sogar.

Despite opposition to the deal, Ghana's president says 40 more deportees will arrive from the US.

Auf der Ferieninsel Mallorca gibts einen Immobilien-Boom! Jetzt wird sogar eine Geistersiedlung wieder zum Leben erweckt, die eigentlich längst vergessen war.