The United States military has said it carried out multiple air strikes in eastern Syria against Iran-aligned groups who it blamed for a deadly drone attack earlier that killed a contractor, injured another, and wounded five US troops, the Pentagon said. The US attacks late on Thursday night were in retaliation for an attack against a US-led coalition base near Hassakeh in northeast Syria at approximately 01:38pm (10:38 GMT) the same day, the Pentagon said in a statement. US intelligence has assessed that the drone was Iranian in origin and US Defence Secretary Lloyd Austin said the strikes targeted groups affiliated with Iran's Islamic Revolutionary Guards Corps in eastern Syria. "The airstrikes were conducted in response to today's attack as well as a series of recent attacks against Coalition forces in Syria by groups affiliated with the IRGC," Austin said in a statement. Austin said he authorised the retaliatory strikes at the direction of US President Joe Biden.

Building destruction during war is a specific form of violence which is particularly harmful to civilians, commonly used to displace populations, and therefore warrants special attention. Yet, data from war-ridden areas are typically scarce, often incomplete and highly contested, when available. The lack of such data from conflict zones severely limits media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, as well as the study of violent conflict in academic research. One approach has been to use remote sensing to identify destruction in satellite images[1]. This approach is gaining momentum as high-resolution imagery is becoming readily available and is updated ever quicker yielding weekly or even daily frequency. At the same time recent methodological advances related to deep learning have provided sophisticated tools to extract data from these images [2, 3, 4, 5].

Terror attacks have been linked in part to online extremist content. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. We model this process using a combination of three contextual dimensions -- religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our approach outperforms a competitive baseline by 10.2% in precision.

Using airdrops to deliver relief to disaster zones may sound like a simple solution, but these missions have proved to be inaccurate, wasteful and expensive. With that in mind, ex-British Army veteran Nigel Gifford is developing a drone with edible wings that is capable of carrying 100-pounds of vacuum-packed food and medical supplies. Although in early stages, the'Pouncer' would be released from a plane or catapult and dropped within a 25 mile radius of the target. Nigel Gifford is developing a drone with edible wings that is capable of carrying 100-pounds of vacuum-packed food and medical supplies. Although in early stages, the'Pouncer' would be released from a plane or catapult and dropped within a 25 mile radius of the target Pouncer is the brain child of engineer and ex-British Army veteran Nigel Gifford.