Jon Kotwicki jokes that converting an aluminum plane in Alaska is the "worst idea that a person could possibly have." This 108-foot-long former cargo plane now has a king size bed, washer dryer, and heated floors, but the build was by no means easy. Breakthroughs, discoveries, and DIY tips sent every weekday. When flight instructor and former commercial airline pilot Jon Kotwicki happened upon a DC-6 air freighter for sale in 2022, he knew it was the perfect plane to transform into an overnight rental. However, once he made the purchase, "my first thought," says Kotwicki, "was, 'My God, what have I done?'" Built in 1956, the 117-foot-wide, 108-foot-long cargo plane had spent its days carrying freight and fuel to remote villages in Alaska before retiring from flight.

Alaska's smallest owl gets the royal treatment after life-threatening injury The reign of the sparrow-sized'King Owlbert' is here. The northern pygmy-owl was found injured in Ketchikan, Alaska. Breakthroughs, discoveries, and DIY tips sent every weekday. Situated on southeast Alaska's scenic Baranof Island, the Alaska Raptor Center in Sitka mainly treats the region's signature bald eagles . These large birds of prey are almost three feet tall and their wingspan is over 6 feet --so one can only imagine how striking it must have been when the staff received a sparrow-sized northern pygmy-owl-Alaska's smallest owl species.

Breakthroughs, discoveries, and DIY tips sent every weekday. At the US Army Corps of Engineers' research facility in central Alaska, a unique tunnel descends underground. They were hunting for something much smaller--and smellier. "The first thing you notice when you walk in there is that it smells really bad. It smells like a musty basement that's been left to sit for way too long," geological scientist Tristan Caro recounted in a statement .

Russia has made gains in Ukraine's Donetsk region before President Vladimir Putin's high-stakes meeting with his United States counterpart Donald Trump in Alaska, raising fears that it may have increased its leverage amid talks aimed at ending the war. In advance of Friday's summit in Anchorage, Moscow's army pounded away at Ukraine's industrial heartland, attempting to seize the flashpoint town of Pokrovsk, a key highway and rail junction in eastern Donetsk, after repeated attempts to breach its defensive line during the week. As Putin and Trump prepared to meet, battlefield analysis site DeepState said that Pokrovsk was partially encircled. In recent days, Russian forces had reportedly seized the village of Yablunivka and the settlement of Oleksandrohrad – both in Donetsk. Ukrainian President Volodymyr Zelenskyy, who has rejected Putin's demands that Kyiv withdraw from the remaining 30 percent of Donetsk that it still controls, played down the Russian advances, saying on X that his forces were "countering" and "increasing the pressure" on the "occupier".

Ukraine's military has said it struck an oil refinery in Russia's Saratov region in an overnight drone attack, causing explosions and destruction, according to an army statement, as daily aerial exchanges intensify with diplomatic momentum to end the war in play. Saratov's governor said on Sunday that one person was killed and several residential apartments and an industrial facility were damaged, but did not mention the oil refinery being struck. "[Ukrainian] drones are targeting … deeper into Russian territory [than] in the past, where previous attacks have been focused on the line of contact in the south and the western parts of Russia," said Al Jazeera's Osama Bin Javaid, reporting from Moscow. It is still unclear whether Ukraine's claims that it hit a refinery are true, he added. Ukraine's military also said on Sunday that it had taken back a village in the Sumy region from the Russian army, which has made significant recent gains there.

Breakthroughs, discoveries, and DIY tips sent every weekday. The Iditarod is the longest annual sled dog race– covering over 1,500 miles across Alaska. A close look into canine genetics reveals sled dogs have been around and on the move for thousands of years. Specifically, the Greenland sled dog–called Qimmeq (singular), or Qimmit (plural) in Greenlandic–has a history traceable all the way back 9,500 years to Zhokhov Island in Eastern Siberia. And they've been a distinct, isolated group for about 1,000 years of that time.

Fine-scale soil mapping in Alaska, traditionally relying on fieldwork and localized simulations, remains a critical yet underdeveloped task, despite the region's ecological importance and extensive permafrost coverage. As permafrost thaw accelerates due to climate change, it threatens infrastructure stability and key ecosystem services, such as soil carbon storage. High-resolution soil maps are essential for characterizing permafrost distribution, identifying vulnerable areas, and informing adaptation strategies. We present MISO, a vision-based machine learning (ML) model to produce statewide fine-scale soil maps for near-surface permafrost and soil taxonomy. The model integrates a geospatial foundation model for visual feature extraction, implicit neural representations for continuous spatial prediction, and contrastive learning for multimodal alignment and geo-location awareness. We compare MISO with Random Forest (RF), a traditional ML model that has been widely used in soil mapping applications. Spatial cross-validation and regional analysis across Permafrost Zones and Major Land Resource Areas (MLRAs) show that MISO generalizes better to remote, unseen locations and achieves higher recall than RF, which is critical for monitoring permafrost thaw and related environmental processes. These findings demonstrate the potential of advanced ML approaches for fine-scale soil mapping and provide practical guidance for future soil sampling and infrastructure planning in permafrost-affected landscapes. The project will be released at https://github.com/knowledge-computing/Peatland-permafrost.

The North Slope of Alaska is dominated by small waterbodies that provide critical ecosystem services for local population and wildlife. Detailed information on the depth of the waterbodies is scarce due to the challenges with collecting such information. In this work we have trained a machine learning (Random Forest Regressor) model to predict depth from multispectral Landsat data in waterbodies across the North Slope of Alaska. The greatest challenge is the scarcity of in situ data, which is expensive and difficult to obtain, to train the model. We overcame this challenge by using modeled depth predictions from a prior study as synthetic training data to provide a more diverse training data pool for the Random Forest. The final Random Forest model was more robust than models trained directly on the in situ data and when applied to 208 Landsat 8 scenes from 2016 to 2018 yielded a map with an overall $r^{2}$ value of 0.76 on validation. The final map has been made available through the Oak Ridge National Laboratory Distribute Active Archive Center (ORNL-DAAC). This map represents a first of its kind regional assessment of waterbody depth with per pixel estimates of depth for the entire North Slope of Alaska.

Retrieval and recommendation are two essential tasks in modern search tools. This paper introduces a novel retrieval-reranking framework leveraging Large Language Models (LLMs) to enhance the spatiotemporal and semantic associated mining and recommendation of relevant unusual climate and environmental events described in news articles and web posts. This framework uses advanced natural language processing techniques to address the limitations of traditional manual curation methods in terms of high labor cost and lack of scalability. Specifically, we explore an optimized solution to employ cutting-edge embedding models for semantically analyzing spatiotemporal events (news) and propose a Geo-Time Re-ranking (GT-R) strategy that integrates multi-faceted criteria including spatial proximity, temporal association, semantic similarity, and category-instructed similarity to rank and identify similar spatiotemporal events. We apply the proposed framework to a dataset of four thousand Local Environmental Observer (LEO) Network events, achieving top performance in recommending similar events among multiple cutting-edge dense retrieval models. The search and recommendation pipeline can be applied to a wide range of similar data search tasks dealing with geospatial and temporal data. We hope that by linking relevant events, we can better aid the general public to gain an enhanced understanding of climate change and its impact on different communities.

It's supposed to be the reliable smart assistant that'makes your life easier' with instant titbits of information. But a shocking report has revealed that in many cases, Amazon's Alexa doesn't know the difference between right and wrong. An investigation by Full Fact has found that Alexa spouts incorrect information on topics ranging from MPs' expenses to the origins of the Northern Lights. Full Fact, the UK's independent fact checking organisation, called the findings'misleading' and'clearly a big problem'. What's more, staff at the organization have been furious to discover that Alexa was attributing the wrong answers to none other than Full Fact.