Fidal

The triple jump is a demanding discipline in athletics, requiring physical capacity to cope with vertical ground reaction force peaks 8-15 times body weight when executed with a full run-up. This study investigated the potential of a specific drill executed with a shortened run up, the "specific quintuple", as a predictor of triple jump performance. A sample of 68 expert athletes participated in a total of218 testing sessions, with measurements taken during both practice sessions and competitions. Linear hierarchical regression analysis revealed a strong correlation between performance of the specific quintuple drill and the triple jump competition performance (R2 = 0.96). This correlation remained significant across different age categories and sexes. The specific quintuple drill offers a safer and more gradual approach to skill acquisition, potentially mitigating the risk of overloading and injury, particularly among youth athletes. The study provides practical insights for coaches and athletes, offering regression equations for predicting triple jump performance based on specific quintuple performance. Overall, the specific quintuple drill emerges as a valuable tool for assessing and improving triple jump performance while prioritizing athletes' safety.

The fundamental characteristic of machine learning (ML) algorithms is their ability to learn to solve problems autonomously, based solely on the data provided to them; to do so, ML models require a huge amount of data (ie training data) to learn how to solve the problems they are subjected to. When talking about the training of artificial intelligence (AI) algorithms, especially for healthcare use, the matter of personal data included in the training datasets cannot be ignored. This includes data belonging to ‘special categories’ (including ‘health data’), which require more robust measures than those put in place for processing of so-called ‘common data’ in order to be processed under the General Data Protection Regulation (GDPR). Among those in the healthcare sector who develop or use AI, this issue is highly relevant. Federated learning is a cooperative ML technique capable of exploiting the knowledge stored in multiple datasets without the need to pool them out, as an innovative approach to privacy-preserving AI model training. In particular, in the healthcare sector, this technique can be used to enable multiple organisations to collaborate without sharing sensitive patient data. The aim of this paper is to showcase how federated learning integrates seamlessly with existing healthcare IT systems to address privacy concerns by presenting use cases that offer a concrete perspective on federated learning’s potential and operational challenge.

This study discusses the interpretation of KH.Misbah Musthofa works. entitled al-Iklil fi Ma’ani al-Tanzil. In this tafsir using the tahlili method and patterns from this tafsir if seen from several opinions have several patterns, but in this writing the writer incorporates Sufistic patterns and al-Ijtima’i manners. In interpreting a verse KH. Misbah Mustofa has a lot through ratio (bi al-Ray’i). The purpose of this study is for the community to always be guided by the Qur’an in life in the world. In addition, this interpretation also criticizes a lot about problems or events that occur in the communityas the author exemplifies the prohibition of family planning. According to KH. Misbah Musthofa by relating the event to the story of Pharaoh. This study is qualitative with a literature analysis from the Qur’an. The results of the study show that the interpretation of al-Iklil fi Ma’ani al-Tanzilis one of the interpretations that have an archipelago pattern with its interpretation using Javanese pegoncharacters. In his interpretation, KH. Misbah Musthofa always explains the meaning of verses clearly and in detail, for example the relationship ofa verse with verses and hadiths. However, when listing a hadith narration, KH. Misbah Musthofa did not include a sanad from the hadith.

The European Union plays a major role in French energy policy. It has triggered liberalization of the markets and promoted energy transition. Yet, the French energy system has proved to be efficient in terms of security of supply and low carbon emissions, thanks to a well-established nuclear industry. This implied a top-down centralized management of energy, which seems to clash with the promotion of renewable energies amenable to a more decentralized system. This chapter highlights existing institutional deadlocks and potential changes as local governance is now enshrined in law.