Edge sensing and computing is rapidly becoming part of intelligent infrastructure architecture leading to operational reliance on such systems in disaster or emergency situations. In such scenarios there is a high chance of power supply failure due to power grid issues, and communication system issues due to base stations losing power or being damaged by the elements, e.g., flooding, wildfires etc. Mobile edge computing in the form of unmanned aerial vehicles (UAVs) has been proposed to provide computation offloading from these devices to conserve their battery, while the use of UAVs as relay network nodes has also been investigated previously. This paper considers the use of UAVs with further constraints on power and connectivity to prolong the life of the network while also ensuring that the data is received from the edge nodes in a timely manner. Reinforcement learning is used to investigate numerous scenarios of various levels of power and communication failure. This approach is able to identify the device most likely to fail in a given scenario, thus providing priority guidance for maintenance personnel. The evacuations of a rural town and urban downtown area are also simulated to demonstrate the effectiveness of the approach at extending the life of the most critical edge devices.

Ensuring a stable power supply in rural areas relies heavily on effective inspection of power equipment, particularly transmission lines (TLs). However, detecting TLs from aerial imagery can be challenging when dealing with misalignments between visible light (RGB) and infrared (IR) images, as well as mismatched high- and low-level features in convolutional networks. To address these limitations, we propose a novel Hierarchical Multi-Modal Enhancement Network (HMMEN) that integrates RGB and IR data for robust and accurate TL detection. Our method introduces two key components: (1) a Mutual Multi-Modal Enhanced Block (MMEB), which fuses and enhances hierarchical RGB and IR feature maps in a coarse-to-fine manner, and (2) a Feature Alignment Block (FAB) that corrects misalignments between decoder outputs and IR feature maps by leveraging deformable convolutions. We employ MobileNet-based encoders for both RGB and IR inputs to accommodate edge-computing constraints and reduce computational overhead. Experimental results on diverse weather and lighting conditionsfog, night, snow, and daytimedemonstrate the superiority and robustness of our approach compared to state-of-the-art methods, resulting in fewer false positives, enhanced boundary delineation, and better overall detection performance. This framework thus shows promise for practical large-scale power line inspections with unmanned aerial vehicles.

An Israeli drone attack on a vehicle near the occupied West Bank town of Qabatiya has killed two people, the Palestinian Ministry of Health says on the fourth day of a large-scale Israeli operation in and around the nearby city of Jenin. The Israeli military said the air attack on Friday in the Jenin governorate hit a vehicle with what it said was a "terrorist cell" inside, but it gave no further details. The official Palestinian news agency Wafa reported that it was a drone attack that happened just before Israeli forces stormed Qabatiya and began "sweeping operations". The air attack coincided with the ongoing military operation against Palestinian fighters in Jenin and its adjacent refugee camp, which has already resulted in the deaths of 14 Palestinians and injured about 50 others, according to the Palestinian Health Ministry in Ramallah. The Israeli military also announced the arrests of 20 people it considers "wanted suspects" and said it had seized weapons.

A New Jersey Mayor has shared new footage of'glowing orbs transforming into drones' over Long Island, adding more intrigue to this ongoing mystery. Michael Melham, the Mayor of Belleville, has been outspoken about the unexplained phenomena plaguing his state and the greater tri-state area since mid-November when the drones first appeared. He shared the bizarre footage on X, saying the clips'appears to show glowing orbs turning into drones. Verified not to be planes via flight tracker. In a recent interview with NewsNation, Melham said he is still getting reports of drone sightings'all over New Jersey, and even Long Island.' 'Here in New Jersey, we are about 500 mayors strong, we are still waiting for answers because our residents are still gravely concerned over what's flying just over our homes,' he said.

People were filmed fleeing a fire at a Russian oil refinery in Ryazan, about 200 kilometres southeast of Moscow, after a Ukrainian drone attack. The facility accounted for almost 5% of the country's oil refining output last year.

Andriy Kovalenko, head of Ukraine's centre for countering disinformation, said on Telegram that an oil refinery in Ryazan had been hit, as well as the Kremniy factory in Bryansk that Kyiv says produces missile components and other weapons. Bloggers on Telegram posted images and videos of fires raging at the Ryazan facility, which covers around 6sq km (2.3sq miles). Verified footage shows people fleeing from the site in cars and on foot as a fireball rises into the sky. BBC Verify used video footage to establish the location of two fires at the refinery. One video shows a fire near the northern entrance, whose location was matched by the road layout, signs and fences.

At least three people have been killed in a Russian drone attack near Ukraine's capital. The interior ministry said on Friday that drone debris killed two men and a woman in the overnight attack on the central Kyiv region, damaging a residential apartment building, eight houses, commercial buildings and several cars. The attack came as Russian authorities said the country's air defence systems intercepted and destroyed 121 drones launched by Ukraine overnight. The drones were downed over 13 Russian regions, including seven over Moscow and the nearby region, the defence ministry said in a statement on Telegram. Moscow Mayor Sergei Sobyanin said the drones had been intercepted at several locations around the capital.

Russian aerial attacks in eastern and central Ukraine killed at least three people and wounded dozens. Among those killed were a 53-year-old in the Kostiantynivka area and a 54-year-old in the northeastern Kharkiv region. The mayor of Russia's Ryazan region, Sergei Sobyanin, said air defence units intercepted three Ukrainian drones headed for Russia's capital Moscow. No damage or casualties were reported, but specialist emergency crews were deployed to the site. Ukraine ordered the evacuation of some 267 children and their families from 16 settlements along the front line in the east of the country that were under threat from advancing Russian forces.

Inspection systems utilizing unmanned aerial vehicles (UAVs) equipped with thermal cameras are increasingly popular for the maintenance of photovoltaic (PV) power plants. However, automation of the inspection task is a challenging problem as it requires precise navigation to capture images from optimal distances and viewing angles. This paper presents a novel localization pipeline that directly integrates PV module detection with UAV navigation, allowing precise positioning during inspection. Detections are used to identify the power plant structures in the image and associate these with the power plant model. We define visually recognizable anchor points for the initial association and use object tracking to discern global associations. We present three distinct methods for visual segmentation of PV modules based on traditional computer vision, deep learning, and their fusion, and we evaluate their performance in relation to the proposed localization pipeline. The presented methods were verified and evaluated using custom aerial inspection data sets, demonstrating their robustness and applicability for real-time navigation. Additionally, we evaluate the influence of the power plant model's precision on the localization methods.

The Unmanned Aerial Vehicle (UAV) path planning problem is a complex optimization problem in the field of robotics. In this paper, we investigate the possible utilization of this problem in benchmarking global optimization methods. We devise a problem instance generator and pick 56 representative instances, which we compare to established benchmarking suits through Exploratory Landscape Analysis to show their uniqueness. For the computational comparison, we select twelve well-performing global optimization techniques from both subfields of stochastic algorithms (evolutionary computation methods) and deterministic algorithms (Dividing RECTangles, or DIRECT-type methods). The experiments were conducted in settings with varying dimensionality and computational budgets. The results were analyzed through several criteria (number of best-found solutions, mean relative error, Friedman ranks) and utilized established statistical tests. The best-ranking methods for the UAV problems were almost universally the top-performing evolutionary techniques from recent competitions on numerical optimization at the Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. Lastly, we discussed the variable dimension characteristics of the studied UAV problems that remain still largely under-investigated.