The severity of natural disasters is increasing every year, impacting many people's lives. During the response phase of disasters, airports are important hubs where relief aid arrives and people need to be evacuated. However, the airport often forms a bottleneck in these relief operations due to the sudden need for increased capacity. Limited research has been done on the operational side of airport disaster management. Experts identify the main problems as, first, the asymmetry of information between the airport and incoming flights, and second, the lack of resources. The goal of this research is to understand the effects of incomplete knowledge of incoming flights with different resource allocation strategies on the performance of cargo handling operations at an airport after a natural disaster. An agent-based model is created, implementing realistic offloading strategies with different degrees of information uncertainty. Model calibration and verification are performed with experts in the field. The model performance is measured by the average turnaround time, which is divided into offloading time, boarding time, and cumulative waiting times. The results show that the effects of one unplanned aircraft are negligible. However, all waiting times increase with more arriving unplanned aircraft.

The Defense Ministry said Thursday that the Air Self-Defense Force scrambled fighter jets 704 times in response to possible airspace violations in fiscal 2024, up by 35 from the previous year. Of the total, scrambles against Chinese military aircraft accounted for 464, or 65.9%, down by 15. In August, Chinese military airplanes violated Japanese airspace off the Danjo Islands in Nagasaki Prefecture for the first time. The number of Chinese drones detected by the ministry more than tripled to 30, exceeding the 26 detected between fiscal 2013, when the first Chinese drone was spotted, and fiscal 2023. "China may have developed a system to (fully) operate drones, upgrading from trial flights," a ministry official said.

Russian transport aircraft have been reported damaged in a drone attack on an airport in Russia's western city of Pskov – located near the borders of Latvia and Estonia – where explosions, a large blaze and gunfire were reported, a local official and state media said. Russia's state-run TASS news agency, quoting emergency services, said early on Wednesday morning that four Il-76 heavy transport aircraft, which have long been the workhorse of the Russian military, were damaged at the airfield in Pskov, located roughly 800km (some 500 miles) from the border with Ukraine. "The defence ministry is repelling a drone attack in Pskov's airport," the regional Governor Mikhail Vedernikov said on the Telegram messaging app, posting a video of a large fire, with sounds of explosions and sirens in the background. Vedernikov, who was at the scene of the attack, said that "according to preliminary information, there are no victims". The scale of the damage to the airport was being assessed, he said.

Shield AI, a San Diego startup that's building artificial intelligence "pilots" for military aircraft and drones, has pulled in an additional $60 million in venture capital funding. The money is follow-on investment to a financing that Shield AI announced in June. It brings the total amount raised in the Series E round to $225 million -- made up of $150 million in equity and $75 million in debt. The additional capital came from the U.S. Innovative Technology Fund. Founded in 2015, Shield AI has raised just under $575 million since inception.

A key part of realizing the future of commercial drones will be drone traffic management: An integrated way to manage airspace for UAV. That's the goal of a recent trial in Japan led by NEDO (National Institute of New Energy and Industrial Technology Development Organization) to develop a drone traffic management system for multiple drone operators to fly in the same airspace safely. The trial, closely watched in the industry, brings together several prominent companies and consortiums, including ANRA Technologies, BIRD INITIATIVE, NEC Corporation, All Nippon Airways (ANA), and other partners. It will take place above Wakkanai City in Japan using ANRA's airspace and delivery management software platforms. The testbed is part of an ongoing R&D effort led by NEDO with the aim of integrating drone traffic management and creating a blueprint for a nationwide traffic management system.

Artificial intelligence helped co-pilot a U-2 "Dragon Lady" spy plane during a test flight Tuesday, the first time artificial intelligence has been used in such a way aboard a US military aircraft. Mastering artificial intelligence or "AI" is increasingly seen as critical to the future of warfare and Air Force officials said Tuesday's training flight represented a major milestone. "The Air Force flew artificial intelligence as a working aircrew member onboard a military aircraft for the first time, December 15," the Air Force said in a statement, saying the flight signaled "a major leap forward for national defense in the digital age." The Artificial Intelligence algorithm, known as "ARTUµ," was developed by researchers at the Air Force's Air Combat Command U-2 Federal Laboratory. The AI system has been "trained ... to execute specific in-flight tasks that otherwise would be done by the pilot," the statement said.

Picture a pilot navigating the crowded skies over a major European city with a dark thundercloud looming ahead. Avoiding the storm is a matter of urgency, but how? Fortunately, the decision to change trajectory has just become easier, thanks to Thales's new-generation Flight Management System (FMS) for civil and military aircraft, PureFlyt, which provides pilots with more detailed weather information as the flight progresses. It does this with agile functions including flight planning and trajectory computation, fuel management, horizontal and vertical guidance, datalink connections with on-ground counterparts, and location capabilities. A user-friendly "What You See Is What You Fly" display shows the pilot precisely how the aircraft is forecast to behave throughout the duration of the flight up until wheels touch ground.

Trump calls the strike a'foolish move'; national security correspondent Jennifer Griffin reports. It's time for the U.S. to take military action against Iran – not to start a war, but to blow some things up in retaliation for Iran shooting down a U.S. surveillance drone Thursday in international air space, just days after setting off explosives that damaged two oil tankers. President Trump gave Iran a pass after the recent tanker attacks. But instead of reassessing their strategy and trying to de-escalate tensions, the Iranians escalated significantly by shooting down the American drone – a high-flying unmanned aircraft that costs about $130 million. I don't see how President Trump can let Iran's latest attack pass without action if he expects Iran and other nations to respect the U.S. and not conclude they can attack our forces at will, without fear of retaliation.

Implementation and Results Introduction Conclusion References Deep Auto-Encoders • 4xNvidia K40 GPUs with with 2880 cores and 12 GB device RAM each in Ubuntu OS workstation •Theano based toolchain for Deep Learning • Nvidia K40 with 12 GB device RAM - driving factor for large dataset inhalation, caching and computation - especially the pre-training stage for DBNs Email:{venugov, gierinmj, reddykk}@utrc.utc.com Deep Belief Nets Layer 1 Layer 2 Bottleneck layer Input layer W2 T Layer 1 Layer 2 RBM RBM RBM Recursive pre-training W1 T W3 T • Successful adoption of Deep Learning methodologies to UTC applications in aerospace and building systems as shown in the timeline. Offers customized support agreements to help operators achieve optimal aircraft utilization. Products range from single actuators to complete flight control systems for the fixed wing, rotorcraft and missile segments as well as fly-by-wire cockpit controls, cabin equipment, trimmable horizontal stabilizer actuators and flight safety parts for helicopters. Engine products include electronic engine controllers, fuel systems, engine actuation, thermal management systems, accessory drive gearboxes and transmissions, drive shafts and flexible couplings, engine start systems, turbine blades and vanes.

It was a good year for imaginative military innovations. From "Star Wars"-style speeders to an inescapable surveillance drone, many of the futuristic advances seem straight out of science fiction or Hollywood blockbusters. Remember those speeder bikes in "Return of the Jedi" that raced through the air? The US military may get to zoom around the battlespace on a type of real-life version in the not-so-distant future. Malloy Aeronautics and SURVICE Engineering Company teamed up to further develop Malloy's Hoverbike for the U.S. Army Research Laboratory.