Generative artificial intelligence (GenAI) holds great promise as a tool to support personalized learning. Teachers need tools to efficiently and effectively enhance content readability of educational texts so that they are matched to individual students reading levels, while retaining key details. Large Language Models (LLMs) show potential to fill this need, but previous research notes multiple shortcomings in current approaches. In this study, we introduced a generalized approach and metrics for the systematic evaluation of the accuracy and consistency in which LLMs, prompting techniques, and a novel multi-agent architecture to simplify sixty informational reading passages, reducing each from the twelfth grade level down to the eighth, sixth, and fourth grade levels. We calculated the degree to which each LLM and prompting technique accurately achieved the targeted grade level for each passage, percentage change in word count, and consistency in maintaining keywords and key phrases (semantic similarity). One-sample t-tests and multiple regression models revealed significant differences in the best performing LLM and prompt technique for each of the four metrics. Both LLMs and prompting techniques demonstrated variable utility in grade level accuracy and consistency of keywords and key phrases when attempting to level content down to the fourth grade reading level. These results demonstrate the promise of the application of LLMs for efficient and precise automated text simplification, the shortcomings of current models and prompting methods in attaining an ideal balance across various evaluation criteria, and a generalizable method to evaluate future systems.

Attacks on underwater cables running through strategically significant bodies of water in both the Baltic Sea and the South China Sea by Russia and China, respectively, in recent months has top officials concerned they are not "mere coincidence." Maritime sabotage efforts in both regions of the world appear to have been on the rise over the last several years, with a notable spike in recent months after at least three separate attacks occurred in as many months, beginning in November, and the top suspects are Russia and China. "The Kremlin has been running a hybrid campaign against Europe for years, ranging from spreading disinformation and cyberattacks to weaponizing energy supplies. Since Russia's full-scale invasion of Ukraine, these efforts have intensified dramatically," EU High Representative Kaja Kallas told Fox News Digital. "However, Russia is not the only challenge we face."

Russia claims it has captured two villages in eastern Ukraine where its forces have been steadily advancing for months, as Ukraine's president urged allies to deliver all the weapons they have promised to send to Kyiv. The Russian Defence Ministry said on Sunday that soldiers have captured the village of Yantarne in the eastern Donetsk region, about 10km (six miles) southwest of Kurakhove, a key logistics hub that Moscow claimed to have seized last week – a day after Russia's army said it had also taken new territory northwest of Kurakhove. The Defence Ministry added that soldiers had also captured the village of Kalinove in the northeastern Kharkiv region. The village is on the western bank of the Oskil River, which for a long time formed the front line between the two armies in the region. A Ukrainian official, quoted by the AFP news agency, said on Thursday that Russian forces had managed to establish a bridgehead on the western bank after crossing the river.

The challenge focused on the data-driven predictability of global sea surface temperatures (SSTs), a key factor in climate forecasting, ecosystem management, fisheries management, and climate change monitoring. The challenge involved forecasting SST anomalies (SSTAs) three months in advance using historical data and included a special task of predicting SSTAs nine months ahead for the Baltic Sea. Participants utilized various machine learning approaches to tackle the task, leveraging data from ERA5. This paper discusses the methodologies employed, the results obtained, and the lessons learned, offering insights into the future of climate-related predictive modeling.

Data-driven models are promising tools for predicting ocean conditions and enhancing the details of these predictions. In this study, we applied advanced machine learning methods to model sea surface velocity and height in the Gulf of Mexico. To forecast broad ocean conditions, we used a method called Fourier Neural Operators (FNO), designed to balance computational efficiency with accuracy through a specialized loss function that combines grid and spectral space information. For creating high-resolution details from low-resolution data -- a process called downscaling -- we explored two different neural network architectures and compared their performance against simpler linear interpolation. This combination of forecasting and downscaling methods greatly improves the efficiency of ocean forecast and downscaling compared to numerical simulation with limited input variables. Our results highlight that these data-driven techniques can provide reliable, physics-aware predictions that can be useful for quick, localized analyses and in generating statistical predictions.

Automatic Ship Identification Systems (AIS) play a key role in monitoring maritime traffic, providing the data necessary for analysis and decision-making. The integrity of this data is fundamental to the correctness of infer-ence and decision-making in the context of maritime safety, traffic manage-ment and environmental protection. This paper analyzes the impact of data integrity in large AIS datasets, on classification accuracy. It also presents er-ror detection and correction methods and data verification techniques that can improve the reliability of AIS systems. The results show that improving the integrity of AIS data significantly improves the quality of inference, which has a direct impact on operational efficiency and safety at sea.

Phytoplankton are a crucial component of aquatic ecosystems, and effective monitoring of them can provide valuable insights into ocean environments and ecosystem changes. Traditional phytoplankton monitoring methods are often complex and lack timely analysis. Therefore, deep learning algorithms offer a promising approach for automated phytoplankton monitoring. However, the lack of large-scale, high-quality training samples has become a major bottleneck in advancing phytoplankton tracking. In this paper, we propose a challenging benchmark dataset, Multiple Phytoplankton Tracking (MPT), which covers diverse background information and variations in motion during observation. The dataset includes 27 species of phytoplankton and zooplankton, 14 different backgrounds to simulate diverse and complex underwater environments, and a total of 140 videos. To enable accurate real-time observation of phytoplankton, we introduce a multi-object tracking method, Deviation-Corrected Multi-Scale Feature Fusion Tracker(DSFT), which addresses issues such as focus shifts during tracking and the loss of small target information when computing frame-to-frame similarity. Specifically, we introduce an additional feature extractor to predict the residuals of the standard feature extractor's output, and compute multi-scale frame-to-frame similarity based on features from different layers of the extractor. Extensive experiments on the MPT have demonstrated the validity of the dataset and the superiority of DSFT in tracking phytoplankton, providing an effective solution for phytoplankton monitoring.

Russia's gradual, grinding advance in parts of Ukraine's eastern region of Donetsk succeeded in wresting away 4,168 sq km (1,609 square miles) of fields and abandoned villages in 2024 – equivalent to 0.69 percent of the country. That was the assessment of the Institute for the Study of War, a Washington-based think-tank, based on satellite imagery and geolocated video footage. "Russian forces have seized four mid-sized settlements – Avdiivka, Selydove, Vuhledar, and Kurakhove – in all of 2024, the largest of which had a pre-war population of just over 31,000 people," said the ISW. Russian forces spent four months taking Avdiivka, and two months each for Selydove and Kurakhove. "Seizing these settlements has not allowed Russian forces to threaten any notable Ukrainian defensive nodes," said the ISW, adding that Moscow's troops failed to conduct the kind of rapid, mechanised manoeuvre necessary to convert these "tactical gains into deep penetrations of Ukraine's rear".

Recently, the remarkable success of ChatGPT has sparked a renewed wave of interest in artificial intelligence (AI), and the advancements in visual language models (VLMs) have pushed this enthusiasm to new heights. Differring from previous AI approaches that generally formulated different tasks as discriminative models, VLMs frame tasks as generative models and align language with visual information, enabling the handling of more challenging problems. The remote sensing (RS) field, a highly practical domain, has also embraced this new trend and introduced several VLM-based RS methods that have demonstrated promising performance and enormous potential. In this paper, we first review the fundamental theories related to VLM, then summarize the datasets constructed for VLMs in remote sensing and the various tasks they addressed. Finally, we categorize the improvement methods into three main parts according to the core components of VLMs and provide a detailed introduction and comparison of these methods. A project associated with this review has been created at https://github.com/taolijie11111/VLMs-in-RS-review.

When it comes to new horror games, there are times of feast and famine, and this past year we gorged until our bellies bulged and our mouths dripped with gruesome grease. In 2024, we received a rich spread of dark experiences from solo creators, indie teams, AA developers and AAA studios in a vast array of genres and visual styles. There was a fantastic Silent Hill 2 remake and beefy updates to contemporary classics like Phasmophobia, Alan Wake 2 and The Outlast Trials, and there was also a steady cadence of brand-new horror franchises expanding the genre in unexpected ways. First, let's take a moment to celebrate a sampling of the year's fresh horror universes. This is not a comprehensive list of new horror franchises in 2024, but it's a suitable demonstration of how vast and varied the offerings were this year.