This study investigates the dissemination of disinformation on social media platforms during the DANA event (DANA is a Spanish acronym for Depresion Aislada en Niveles Altos, translating to high-altitude isolated depression) that resulted in extremely heavy rainfall and devastating floods in Valencia, Spain, on October 29, 2024. We created a novel dataset of 650 TikTok and X posts, which was manually annotated to differentiate between disinformation and trustworthy content. Additionally, a Few-Shot annotation approach with GPT-4o achieved substantial agreement (Cohen's kappa of 0.684) with manual labels. Emotion analysis revealed that disinformation on X is mainly associated with increased sadness and fear, while on TikTok, it correlates with higher levels of anger and disgust. Linguistic analysis using the LIWC dictionary showed that trustworthy content utilizes more articulate and factual language, whereas disinformation employs negations, perceptual words, and personal anecdotes to appear credible. Audio analysis of TikTok posts highlighted distinct patterns: trustworthy audios featured brighter tones and robotic or monotone narration, promoting clarity and credibility, while disinformation audios leveraged tonal variation, emotional depth, and manipulative musical elements to amplify engagement. In detection models, SVM+TF-IDF achieved the highest F1-Score, excelling with limited data. Incorporating audio features into roberta-large-bne improved both Accuracy and F1-Score, surpassing its text-only counterpart and SVM in Accuracy. GPT-4o Few-Shot also performed well, showcasing the potential of large language models for automated disinformation detection. These findings demonstrate the importance of leveraging both textual and audio features for improved disinformation detection on multimodal platforms like TikTok.

In this paper we develop a mathematical model combined with machine learning techniques to predict shade-seeking behavior in cows exposed to heat stress. The approach integrates advanced mathematical features, such as time-averaged thermal indices and accumulated heat stress metrics, obtained by mathematical analysis of data from a farm in Titaguas (Valencia, Spain), collected during the summer of 2023. Two predictive models, Random Forests and Neural Networks, are compared for accuracy, robustness, and interpretability. The Random Forest model is highlighted for its balance between precision and explainability, achieving an RMSE of $14.97$. The methodology also employs $5-$fold cross-validation to ensure robustness under real-world conditions. This work not only advances the mathematical modeling of animal behavior but also provides useful insights for mitigating heat stress in livestock through data-driven tools.

Researchers have spotted an apparent downside of smarter chatbots. Although AI models predictably become more accurate as they advance, they're also more likely to (wrongly) answer questions beyond their capabilities rather than saying, "I don't know." And the humans prompting them are more likely to take their confident hallucinations at face value, creating a trickle-down effect of confident misinformation. "They are answering almost everything these days," José Hernández-Orallo, professor at the Universitat Politecnica de Valencia, Spain, told Nature. "And that means more correct, but also more incorrect." Hernández-Orallo, the project lead, worked on the study with his colleagues at the Valencian Research Institute for Artificial Intelligence in Spain.

Large language models (LLMs) seem to get less reliable at answering simple questions when they get bigger and learn from human feedback. AI developers try to improve the power of LLMs in two main ways: scaling up – giving them more training data and more computational power – and shaping up, or fine-tuning them in response to human feedback. How does ChatGPT work and do AI-powered chatbots "think" like us? José Hernández-Orallo at the Polytechnic University of Valencia, Spain, and his colleagues examined the performance of LLMs as they scaled up and shaped up. They looked at OpenAI's GPT series of chatbots, Meta's LLaMA AI models, and BLOOM, developed by a group of researchers called BigScience. The researchers tested the AIs by posing five types of task: arithmetic problems, solving anagrams, geographical questions, scientific challenges and pulling out information from disorganised lists.

Machine Listening focuses on developing technologies to extract relevant information from audio signals. A critical aspect of these projects is the acquisition and labeling of contextualized data, which is inherently complex and requires specific resources and strategies. Despite the availability of some audio datasets, many are unsuitable for commercial applications. The paper emphasizes the importance of Active Learning (AL) using expert labelers over crowdsourcing, which often lacks detailed insights into dataset structures. AL is an iterative process combining human labelers and AI models to optimize the labeling budget by intelligently selecting samples for human review. This approach addresses the challenge of handling large, constantly growing datasets that exceed available computational resources and memory. The paper presents a comprehensive data-centric framework for Machine Listening projects, detailing the configuration of recording nodes, database structure, and labeling budget optimization in resource-constrained scenarios. Applied to an industrial port in Valencia, Spain, the framework successfully labeled 6540 ten-second audio samples over five months with a small team, demonstrating its effectiveness and adaptability to various resource availability situations.

Urban traffic emissions represent a significant concern due to their detrimental impacts on both public health and the environment. Consequently, decision-makers have flagged their reduction as a crucial goal. In this study, we first analyze the correlation between traffic flux and pollution in the city of Valencia, Spain. Our results demonstrate that traffic has a significant impact on the levels of certain pollutants (especially $\text{NO}_\text{x}$). Secondly, we develop an alarm system to predict if a street is likely to experience unusually high traffic in the next 30 minutes, using an independent three-tier level for each street. To make the predictions, we use traffic data updated every 10 minutes and Long Short-Term Memory (LSTM) neural networks. We trained the LSTM using traffic data from 2018, and tested it using traffic data from 2019.

Images of lounging elephants, treefrog embryos and a parasitic fungus erupting from the body of a fly have all won prizes at an ecology photo competition. The doomed fly was captured by evolutionary biologist Roberto García-Roa in the Tambopata National Reserve, Peru, and took the overall win at the second ever BMC Ecology and Evolution Image Competition. The contest aims to showcase the wonder of the natural world and emphasise the growing need to protect it from human activity. Mr García-Roa, from the University of Valencia, Spain, said: 'The image depicts a conquest that has been shaped by thousands of years of evolution. 'The spores of the so-called'zombie' fungus have infiltrated the exoskeleton and mind of the fly and compelled it to migrate to a location that is more favourable for the fungus's growth.

Despite continuing controversies over its value in improving birth rates in IVF, testing embryos for their chromosomal content has become routine in many fertility clinics. Embryos with a normal complement of chromosomes (known as "euploid") are known to have a good chance of implanting in the uterus to become a pregnancy, while abnormal embryos (aneuploid) have no chance. Testing embryos for aneuploidy (known as PGT-A) has so far required a sample single cell or several cells taken from the embryo by biopsy, and this too has raised fears over safety such that a search for non-invasive methods has arisen in recent years. Now, a new study suggests that euploid embryos can be visually distinguished from aneuploid according to artificial intelligence references of cell activity as seen by time-lapse imaging--and thus without the need for cell biopsy. The results of the study will be presented today at the online annual meeting of ESHRE by Ms Lorena Bori from IVIRMA in Valencia, Spain, on behalf a joint research team from IVIRMA Valencia and AIVF, Israel, co-directed by Dr. Marcos Meseguer from Valencia and Dr. Daniella Gilboa from Tel-Aviv.

With my co-authors Pablo Torres, Sergio Hoyas (both from Instituto Universitario de Matemática Pura y Aplicada, Universitat Politécnica de Valencia, Spain) and Ricardo Vinuesa (from Engineering Mechanics, KTH Royal Institute of Technology, Sweden), we have written a book chapter which focuses on the key role of machine learning (ML) methods to analyze air quality and air flow in urban environments (especially in dense cities) which might be an indicator of public health [1]. We have provided a review of the ML methods used in this field and we have highlighted the relevance of the urban air quality and air flow to the number of hospitalizations and respiratory diseases as they were reported in the literature. Here is a mini lecture which summarizes our book chapter in a video [4].