The same pulling force that causes "tears" in a glass of wine also shapes embryos. It's another example of how genes exploit mechanical forces for growth and development. Sip a glass of wine, and you will notice liquid continuously weeping down the wetted side of the glass. In 1855, James Thomson, brother of Lord Kelvin, explained in the that these wine "tears" or "legs" result from the difference in surface tension between alcohol and water. "This fact affords an explanation of several very curious motions," Thomson wrote.

The Turtle Conservancy is rescuing reptiles, while preserving their precious DNA. Breakthroughs, discoveries, and DIY tips sent every weekday. Nestled in rural New Jersey's rolling hills, a top secret animal sanctuary is keeping its occupants safe. The oasis is not for the official state animal (the horse) or even birds rescued from the Jersey shore. This hidden refuge belongs to turtles and tortoises.

This study explores the integration of Augmented Intelligence (AuI) in Intelligent Tutoring Systems (ITS) to address challenges in Artificial Intelligence in Education (AIED), including teacher involvement, AI reliability, and resource accessibility. We present MathAIde, an ITS that uses computer vision and AI to correct mathematics exercises from student work photos and provide feedback. The system was designed through a collaborative process involving brainstorming with teachers, high-fidelity prototyping, A/B testing, and a real-world case study. Findings emphasize the importance of a teacher-centered, user-driven approach, where AI suggests remediation alternatives while teachers retain decision-making. Results highlight efficiency, usability, and adoption potential in classroom contexts, particularly in resource-limited environments. The study contributes practical insights into designing ITSs that balance user needs and technological feasibility, while advancing AIED research by demonstrating the effectiveness of a mixed-methods, user-centered approach to implementing AuI in educational technologies.

Retrieval-augmented generation resorts to content retrieved from external sources in order to leverage the performance of large language models in downstream tasks. The excessive volume of retrieved content, the possible dispersion of its parts, or their out of focus range may happen nevertheless to eventually have a detrimental rather than an incremental effect. To mitigate this issue and improve retrieval-augmented generation, we propose a method to refine the retrieved content before it is included in the prompt by resorting to meta-prompting optimization. Put to empirical test with the demanding multi-hop question answering task from the StrategyQA dataset, the evaluation results indicate that this method outperforms a similar retrieval-augmented system but without this method by over 30 %. Keywords: RAG Retrieval-Augmented Generation Prompt Optimization Large Language Models Meta-prompting Multi-hop QA

This research introduces a Machine Learning-centric approach to replicate olfactory experiences, validated through experimental quantification of perfume perception. Key contributions encompass a hybrid model connecting perfume molecular structure to human olfactory perception. This model includes an AI-driven molecule generator (utilizing Graph and Generative Neural Networks), quantification and prediction of odor intensity, and refinery of optimal solvent and molecule combinations for desired fragrances. Additionally, a thermodynamic-based model establishes a link between olfactory perception and liquid-phase concentrations. The methodology employs Transfer Learning and selects the most suitable molecules based on vapor pressure and fragrance notes. Ultimately, a mathematical optimization problem is formulated to minimize discrepancies between new and target olfactory experiences. The methodology is validated by reproducing two distinct olfactory experiences using available experimental data.

São Paulo, Brazil – As the city of São Paulo prepares to roll out thousands of surveillance cameras with facial recognition, experts are raising concerns on the indiscriminate use of this technology in the Brazilian megalopolis could exacerbate problems such as structural racism and inequality, while also posing risks to data privacy and cybersecurity. The Smart Sampa project is the latest among a series of initiatives involving modern surveillance techniques in various Brazilian states. It is significant due to the sheer size of the population it will impact: São Paulo, the most populous city in the Southern Hemisphere, is home to 12 million people. The project aims to roll out a single video surveillance platform that integrates and supports the operations of emergency and traffic services, the city's public transport network, and police forces. By 2024, up to 20,000 cameras will be installed, and an equal number of third-party and private cameras will be integrated into the network.

This work proposes the use of Genetic Algorithms (GA) in tracing and recognizing the pericardium contour of the human heart using Computed Tomography (CT) images. We assume that each slice of the pericardium can be modelled by an ellipse, the parameters of which need to be optimally determined. An optimal ellipse would be one that closely follows the pericardium contour and, consequently, separates appropriately the epicardial and mediastinal fats of the human heart. Tracing and automatically identifying the pericardium contour aids in medical diagnosis. Usually, this process is done manually or not done at all due to the effort required. Besides, detecting the pericardium may improve previously proposed automated methodologies that separate the two types of fat associated to the human heart. Quantification of these fats provides important health risk marker information, as they are associated with the development of certain cardiovascular pathologies. Finally, we conclude that GA offers satisfiable solutions in a feasible amount of processing time.

Drones that fly packages straight to people's doors could be an environmentally friendly alternative to conventional modes of transportation. A study comparing the environmental impact of various'last-mile' delivery methods -- which take a package on the final leg of its journey -- finds that greenhouse-gas emissions per parcel were 84% lower for drones than for diesel trucks. Drones also consumed up to 94% less energy per parcel than did the trucks. The research, published on 5 August in the journal Patterns, indicates that using drones to deliver medication and other small items could cut the environmental impact of product deliveries. Major companies such as Amazon have been experimenting with using drones and robots to deliver packages with an eye to reducing their environmental impact.

In this paper we present a logical formalism for the treatment of pragmatic ambiguity in spatial expressions of the frontal axis (front/back). The ambiguity occurs because the same situation can be analyzed from different points of view. For this, we use frames of reference for the interpretation of front/back (intrinsic, extrinsic, deictic) together with formalisms of qualitative spatial reasoning.

OceanX, a team of marine biologists, media and filmmakers, embarked on a quest in 2020 to explore the depths of the Red Sea where they not only found a giant shipwreck, but a massive creature that appeared to be larger than a human. While investigating the'Pella,' which sank in November 2011, at a depth of 2,800 feet, the group spotted what they thought could be'The Giant Squid.' 'I will never forget what happened next for as long as I live,' said OceanX science program lead Mattie Rodrigue in a video taken of the discovery. 'All of a sudden, as we're looking at the bow of the shipwreck, this massive creature comes into view, takes a look at the ROV [remotely operated vehicle] and curls its entire body around the bow of the wreck.' It was not until September 2021 did the team learn that the mysterious creature was'the giant form' of the purpleback flying squid, which typically grow up to two feet long. The OceanX team traveled to the Red Sea aboard the OceanXplorer, a research vessel with a 40-ton crane to launch submersibles, towed sonar arrays and other heavy equipment down into the depths.