A ship carrying aid to Gaza in a bid to break Israel's blockade has been hit by drones in international waters off Malta, according to the Freedom Flotilla Coalition (FFC), the group that organised the mission. The FFC said in a statement on Friday that the vessel, now located 14 nautical miles (25km) from Malta, was the target of two drone strikes while on its way to Gaza. The ship had been seeking to deliver aid to the besieged enclave, where aid groups warn people are struggling to survive following a two-month total blockade by Israel. "Armed drones attacked the front of an unarmed civilian vessel twice, causing a fire and a substantial breach in the hull," the group said. The statement did not directly accuse Israel of carrying out the attack.

The NGO appeared to accuse Israel of being behind the incident and called for Israeli ambassadors to be summoned to answer for "violation of international law, including the ongoing blockade and the bombing of our civilian vessel". The Israeli military said it was looking into reports of the attack. The Freedom Flotilla Coalition uploaded a video showing a fire on one of its ships but did not indicate whether anyone had been hurt. It said the attack appeared to have targeted the generator, which left the ship without power and at risk of sinking. The ship was 17 nautical miles (31.5 kilometres) east of Malta when it was hit.

This raises the fundamental question of whether it is possible to systematically differentiate reputable projects from those that may not be. While existing research has primarily focused on detecting fraudulent activities--such as scams, Ponzi schemes, and network anomalies--these efforts remain centered on identifying and flagging illicit behavior rather than providing a holistic assessment of a project's overall reputability. Several studies have explored the detection of illicit activities on the Ethereum blockchain [8], the identification of Ponzi schemes [17], for anti-money laundering [15] and anomaly detection within the network [13]. While these contributions enhance our understanding of fraudulent behavior, they do not directly address the broader issue of evaluating whether a project itself is reputable. Given the growing number of Ethereum-based initiatives, the need for a systematic approach to assessing project reputability becomes increasingly evident. Distinguishing between legitimate and potentially deceptive ventures requires a dedicated methodology that extends beyond merely detecting illicit activity. By establishing such an approach, stakeholders, including investors, developers, and regulators can make more informed decisions, mitigate risks associated with unreliable projects, and foster a more secure and transparent investment landscape within the Ethereum ecosystem. This research aims to identify projects that are likely to be reputable by comparing them against a model comprised of data associated with a list of reputable projects from a source deemed to be trust-worthy. We therefore, define the following project aim to: develop a comprehensive methodology for identifying likely-reputable Ethereum Blockchain based projects using transactional data and machine learning techniques.

This research is focused on traffic congestion in the small island of Malta which is the most densely populated country in the EU with about 1,672 inhabitants per square kilometre (4,331 inhabitants/sq mi). Furthermore, Malta has a rapid vehicle growth. Based on our research, the number of vehicles increased by around 11,000 in a little more than 6 months, which shows how important it is to have an accurate and comprehensive means of collecting data to tackle the issue of fluctuating traffic in Malta. In this paper, we first present the newly built comprehensive traffic dataset, called MalTra. This dataset includes realistic trips made by members of the public across the island over a period of 200 days. We then describe the methodology we adopted to generate syntactic data to complete our data set as much as possible. In our research, we consider both MalTra and the Q-Traffic dataset, which has been used in several other research studies. The statistical ARIMA model and two graph neural networks, the spatial temporal graph convolutional network (STGCN) and the diffusion convolutional recurrent network (DCRNN) were used to analyse and compare the results with existing research. From the evaluation, we found that the DCRNN model outperforms the STGCN with the former resulting in MAE of 3.98 (6.65 in the case of the latter) and a RMSE of 7.78 (against 12.73 of the latter).

Brits were today urged not to get behind the wheel after just one drink, with doctors warning booze has got stronger. The British Medical Association (BMA)'s president said the idea of'getting away' with two pints'has always been dangerous'. However, he cautioned that a 125ml glass of 9 per cent wine -- more common when current drink-driving laws were devised in the 1960s -- is now'virtually unheard of'. This would equate to just over one unit. As a basic guide, men are advised not to drink more than three units before driving, while women should stick to two at a maximum.

We are TRASYS International, NRB Group, an ICT company with over 30 years of a successful track record working with European Institutions and Agencies, offering IT consulting, solutions and services. Our Mission is to help our clients keep up with the challenges of digital transformation by providing the right talent at the right time for the right job. To this end, we are constantly looking for talented professionals who are interested in working on challenging international projects and able to deliver high-quality results within multicultural environments. Our services include (but are not limited to) modernisation solutions, digital workspaces, cloud technologies and IT security. Our Headquarters are in Brussels, and we have active accounts and offices across Europe (i.e.

Syngenta Group is a $28B leading science-based agtech company, operating in more than 100 countries, with more than 50'000 employees. We are proud to stand at the forefront of the tech revolution in agriculture. Using the latest digital innovations, data, and cutting-edge technologies we want to transform the way that crops are managed and enable farmers and agronomists to enhance efficiency and sustainable food production. Our business success reflects the quality and skill of our people. We recognize that human diversity is as important to our business as biodiversity.

Recent years have seen an increased interest in the computational speech processing of Maltese, but resources remain sparse. In this paper, we consider data augmentation techniques for improving speech recognition for low-resource languages, focusing on Maltese as a test case. We consider three different types of data augmentation: unsupervised training, multilingual training and the use of synthesized speech as training data. The goal is to determine which of these techniques, or combination of them, is the most effective to improve speech recognition for languages where the starting point is a small corpus of approximately 7 hours of transcribed speech. Our results show that combining the data augmentation techniques studied here lead us to an absolute WER improvement of 15% without the use of a language model.

Artificial Intelligence (AI) is rapidly changing the way we live, work and learn. If you're looking into ways how you can pursue a career in this booming field, then consider our popular MSc in AI degree programme. Through this 4-semester, part-time programme of studies you will learn the skills you need as it consists of both a taught and a research component. Lectures for the taught component are held after 17:00 to allow people that are already working in the IT industry (and not only) to follow the MSc. The course content aims to further improve your knowledge and expertise in AI.

According to the World Stroke Organisation, there are approximately 13.7 million new cases of stroke each year. To some extent, this may not be news to the vast majority of people. In all likelihood, the average reader will have come across someone who suffered a stroke, whether within immediate family or merely acquaintances. However, what very few people know is that at the heart of all these cases is a pervasive condition called Atherosclerosis – which lies silent and asymptomatic in most people for many years, before it finally surfaces with debilitating consequences. Atherosclerosis is a vascular disease which is characterised by the thickening and hardening of blood vessel walls.