North Korea tested a nuclear-capable underwater attack drone designed to destroy naval vessels and ports, it said Friday. North Korea's military said it conducted the test in the country's eastern waters in response to naval drills by the U.S., South Korea and Japan which ended Wednesday. The underwater drone is among a broad range of weapon systems North Korean dictator Kim Jong Un continues to test and develop as he expands his arsenal of nuclear-capable weapons. "Our army's underwater nuke-based countering posture is being further rounded off and its various maritime and underwater responsive actions will continue to deter the hostile military maneuvers of the navies of the U.S. and its allies," North Korea's Defense Ministry said in a statement. It added: "We strongly denounce the U.S. and its followers for their reckless acts of seriously threatening the security of (North Korea) from the outset of the year and sternly warn them of the catastrophic consequences to be entailed by them."

Former President Trump joins'Hannity' for his first interview since his historic Iowa win to discuss his vision for America and previews the New Hampshire primary. Former President Trump objected to critics' arguments against presidential immunity, saying Thursday either himself or a future president could be stymied in urgent situations by circumspection around whether their executive actions might lead to punishment. Trump told Fox News that, should presidential immunity be muted, when a president is taking unilateral actions as chief executive, the opposing political party could immediately begin strategizing how to prosecute their rival. I'm talking about [how] any president has to have immunity, because if you take immunity away from the president, it's so important, you will have you have a president that's not going to be able to do anything," he said on "Hannity." "[W]hen he leaves office… the opposing party will indict the president for doing something that should have been good," he said, pointing to reports of mistakes or misfires made by his predecessor trying to eradicate terrorists. "Obama dropped missiles and they ended up hitting a kindergarten or a school or an apartment house.

Swarm behaviour engineering is an area of research that seeks to investigate methods and techniques for coordinating computation and action within groups of simple agents to achieve complex global goals like pattern formation, collective movement, clustering, and distributed sensing. Despite recent progress in the analysis and engineering of swarms (of drones, robots, vehicles), there is still a need for general design and implementation methods and tools that can be used to define complex swarm behaviour in a principled way. To contribute to this quest, this article proposes a new field-based coordination approach, called MacroSwarm, to design and program swarm behaviour in terms of reusable and fully composable functional blocks embedding collective computation and coordination. Based on the macroprogramming paradigm of aggregate computing, MacroSwarm builds on the idea of expressing each swarm behaviour block as a pure function mapping sensing fields into actuation goal fields, e.g. including movement vectors. In order to demonstrate the expressiveness, compositionality, and practicality of MacroSwarm as a framework for collective intelligence, we perform a variety of simulations covering common patterns of flocking, morphogenesis, and collective decision-making.

Aerial field robotics research represents the domain of study that aims to equip unmanned aerial vehicles--and as it pertains to this chapter, specifically Micro Aerial Vehicles (MAVs)--with the ability to operate in real-life environments that present challenges to safe navigation. We present the key elements of autonomy for MAVs that are resilient to collisions and sensing degradation, while operating under constrained computational resources. We overview aspects of the state of the art, outline bottlenecks to resilient navigation autonomy, and overview the field-readiness of MAVs. We conclude with notable contributions and discuss considerations for future research that are essential for resilience in aerial robotics. The state of the art in aerial robotics can accomplish impressive tasks. Yet wider use and adoption of MAVs for effective field deployment is limited by the resilience of the components of autonomy. In this chapter, we view each element of the autonomy system under the framework of resilience and examine the latest developments, as well as open questions. Towards a principled understanding of progress in resilient and field-hardened aerial robotic autonomy, we define resilience motivated by analogous studies in the domain of risk analysis (Howell 2013).

In response to the gap in considering wind conditions in the bridge inspection using unmanned aerial vehicle (UAV) , this paper proposes a path planning method for UAVs that takes into account the influence of wind, based on the simulated annealing algorithm. The algorithm considers the wind factors, including the influence of different wind speeds and directions at the same time on the path planning of the UAV. Firstly, An environment model is constructed specifically for UAV bridge inspection, taking into account the various objective functions and constraint conditions of UAVs. A more sophisticated and precise mathematical model is then developed based on this environmental model to enable efficient and effective UAV path planning. Secondly, the bridge separation planning model is applied in a novel way, and a series of parameters are simulated, including the adjustment of the initial temperature value. The experimental results demonstrate that, compared with traditional local search algorithms, the proposed method achieves a cost reduction of 30.05\% and significantly improves effectiveness. Compared to path planning methods that do not consider wind factors, the proposed approach yields more realistic and practical results for UAV applications, as demonstrated by its improved effectiveness in simulations. These findings highlight the value of our method in facilitating more accurate and efficient UAV path planning in wind-prone environments.

Iran and Pakistan have carried out air attacks on each other's territories, targeting armed groups near their 900km-long (559-mile) volatile border, which they say were meant to ensure their respective national security. Iran's powerful Islamic Revolutionary Guard Corps (IRGC) targeted an armed group in Panjgur town of Pakistan's Balochistan province late on Tuesday, prompting Pakistan to bomb hideouts of armed groups in the Sistan-Baluchestan province of Iran early on Thursday. Let's take a look at why the neighbours have resorted to direct military strikes, who the targets were, and what the attacks tell us. The IRGC, an elite force which is a vital part of the Iranian establishment but separate from Iran's army, hit the Jaish al-Adl armed group with missile and drone strikes in a mountainous region in Pakistan close to the Iranian border. Iran said it targeted the Iranian "terrorist" group it blamed for recent attacks in the Iranian city of Rask in the southeastern province of Sistan-Baluchestan.

Fox News' Alexis McAdams reports on how the NYPD is managing protests in New York City since the Oct. 7, 2023, attacks in Israel. Drones used to be fancy gadgets for hobbyists or secret weapons for the military. But now they have a new job: delivering drugs. Yes, you heard that right. While El Pollo Loco is using drones to bring you chicken dinners, some bad guys are using them to smuggle drugs across borders.

JERUSALEM – Beginning Oct. 7, when Hamas terrorists used remote controlled drones to disarm tanks and knock out surveillance cameras during its surprise attack on Israel, through to last week, when a Hezbollah drone from Lebanon landed directly in an army base in northern Israel, unmanned aerial vehicles (UAVs) are increasingly becoming part of the weapons arsenal used by Iranian-backed non-state players in their war against the Jewish state. While Israel has in place what it calls "an aerial defense array" – used multiple times over the past three months to thwart "hostile aircraft" from Gaza and Lebanon – as UAVs become easier to obtain, manufacture, enhance and weaponize, Israel, as well as other countries around the world, are racing to contend with an ever more lethal form of combat that is already outpacing existing military defense systems. "The Israeli – and the U.S. – militaries have been using drones for a long time, especially in counterterrorism, for intelligence gathering or for precision strikes in order to distinguish between civilians and fighters," Dr. Liran Antebi, program director of advanced technologies and national security at the Institute for National Security Studies in Tel Aviv, told Fox News Digital. Xtend's Griffon Counter UAVs, with speeds of up to 93.1 miles per hour, and AI technology are being used by the IDF to identify and kill rogue drones. "However, what was once the silver bullet used by democracies in counterterrorism and to act in more ethical ways, is now in the hands of terrorists or non-democratic states and is being used in the opposite way," she said.

Vision is a major component in several digital technologies and tools used in agriculture. The object detector, You Look Only Once (YOLO), has gained popularity in agriculture in a relatively short span due to its state-of-the-art performance. YOLO offers real-time detection with good accuracy and is implemented in various agricultural tasks, including monitoring, surveillance, sensing, automation, and robotics. The research and application of YOLO in agriculture are accelerating rapidly but are fragmented and multidisciplinary. Moreover, the performance characteristics (i.e., accuracy, speed, computation) of the object detector influence the rate of technology implementation and adoption in agriculture. Thus, the study aims to collect extensive literature to document and critically evaluate the advances and application of YOLO for agricultural object recognition. First, we conducted a bibliometric review of 257 articles to understand the scholarly landscape of YOLO in agricultural domain. Secondly, we conducted a systematic review of 30 articles to identify current knowledge, gaps, and modifications in YOLO for specific agricultural tasks. The study critically assesses and summarizes the information on YOLO's end-to-end learning approach, including data acquisition, processing, network modification, integration, and deployment. We also discussed task-specific YOLO algorithm modification and integration to meet the agricultural object or environment-specific challenges. In general, YOLO-integrated digital tools and technologies show the potential for real-time, automated monitoring, surveillance, and object handling to reduce labor, production cost, and environmental impact while maximizing resource efficiency. The study provides detailed documentation and significantly advances the existing knowledge on applying YOLO in agriculture, which can greatly benefit the scientific community.

Real-time high-resolution wind predictions are beneficial for various applications including safe manned and unmanned aviation. Current weather models require too much compute and lack the necessary predictive capabilities as they are valid only at the scale of multiple kilometers and hours - much lower spatial and temporal resolutions than these applications require. Our work, for the first time, demonstrates the ability to predict low-altitude wind in real-time on limited-compute devices, from only sparse measurement data. We train a neural network, WindSeer, using only synthetic data from computational fluid dynamics simulations and show that it can successfully predict real wind fields over terrain with known topography from just a few noisy and spatially clustered wind measurements. WindSeer can generate accurate predictions at different resolutions and domain sizes on previously unseen topography without retraining. We demonstrate that the model successfully predicts historical wind data collected by weather stations and wind measured onboard drones.