Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Hollywood icon who starred in Psycho after Hitchcock dubbed her'my new Grace Kelly' looks incredible at 95 Alexander brothers' alleged HIGH SCHOOL gang rape video: Classmates speak out on sick'taking turns' footage... as creepy unseen photos are exposed Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting NFL superstar Xavier Worthy spills all on Travis Kelce, the Chiefs' struggles... and having Taylor Swift as his No 1 fan Heartbreaking video shows very elderly DoorDash driver shuffle down customer's driveway with coffee order because he is too poor to retire Amber Valletta, 52, was a '90s Vogue model who made movies with Sandra Bullock and Kate Hudson, see her now Nancy Mace throws herself into Iran warzone as she goes rogue on Middle East rescue mission: 'I AM that person' Global warming has accelerated'significantly' since 2015, study reveals - as scientists call for urgent action to curb CO2 emissions Global warming has accelerated'significantly' since 2015, a new study has revealed. Researchers from the Potsdam Institute for Climate Impact Research used five large global temperature datasets to understand how warming has changed through the years. Their results show that from 1970 to 2015, Earth warmed at a rate of just under 0.2 C (0.36 F) per decade. However, over the past 10 years, this rate has jumped to around 0.35 C (0.63 F) per decade. This is higher than any previous decade since recording began in 1880.

The AI boom has caused as much carbon dioxide to be released into the atmosphere in 2025 as emitted by the whole of New York City, it has been claimed. The global environmental impact of the rapidly spreading technology has been estimated in research published on Wednesday, which also found that AI-related water use now exceeds the entirety of global bottled-water demand. The figures have been compiled by the Dutch academic Alex de Vries-Gao, the founder of Digiconomist, a company that researches the unintended consequences of digital trends. He claimed they were the first attempt to measure the specific effect of artificial intelligence rather than datacentres in general as the use of chatbots such as OpenAIâ s ChatGPT and Googleâ s Gemini soared in 2025. The figures show the estimated greenhouse gas emissions from AI use are also now equivalent to more than 8% of global aviation emissions.

Data Science and Engineering Lab Universit e libre de Bruxelles Brussels, Belgium gaspard.merten@ulb.be Data Science and Engineering Lab Universit e libre de Bruxelles Brussels, Belgium gilles.dejaegere@ulb.be Data Science and Engineering Lab Universit e libre de Bruxelles Brussels, Belgium mahmoud.sakr@ulb.be Abstract --Recent research in Large Language Models (LLMs), has had a profound impact across various fields, including mobility data science. This paper explores the and experiment with different approaches to using LLMs for analyzing AIS data. We propose a set of carefully designed queries to assess the reasoning capabilities of LLMs in this kind of tasks. Further, we experiment with four different methods: (1) using LLMs as a natural language interface to a spatial database, (2) reasoning on raw data, (3) reasoning on compressed trajectories, and (4) reasoning on semantic trajectories. We investigate the strengths and weaknesses for the four methods, and discuss the findings. The goal is to provide valuable insights for both researchers and practitioners on selecting the most appropriate LLM-based method depending on their specific data analysis objectives. The significant development in artificial machine learning has also opened the way to new approaches to solve real-world geospatial problems. In particular, Large Language Models (LLMs) have emerged as powerful tools for understanding and generating human-like text. These models have demonstrated remarkable abilities in natural language processing tasks, from answering complex queries to summarizing and interpreting information in various domains. This exponential increase of LLMs usage can also be witnessed in the domain of Geographic Information Systems (GIS) in recent years.

A new study, published in Frontiers, aims to draw more attention to the issue. Researchers analyzed the number of "tokens"--the smallest units of data that a language model uses to process and generate text--required to produce responses, and found that certain prompts can release up to 50 times more CO2 emissions than others. Different AI models use a different number of parameters; those with more parameters often perform better. The study examined 14 large language models (LLMs) ranging from seven to 72 billion parameters, asking them the same 1,000 benchmark questions across a range of subjects. Parameters are the internal variables that a model learns during training, and then uses to produce results.

Telecom services are at the core of today's societies' everyday needs. The availability of numerous online forums and discussion platforms enables telecom providers to improve their services by exploring the views of their customers to learn about common issues that the customers face. Natural Language Processing (NLP) tools can be used to process the free text collected. One way of working with such data is to represent text as numerical vectors using one of many word embedding models based on neural networks. This research uses a novel dataset of telecom customers' reviews to perform an extensive study showing how different word embedding algorithms can affect the text classification process. Several state-of-the-art word embedding techniques are considered, including BERT, Word2Vec and Doc2Vec, coupled with several classification algorithms. The important issue of feature engineering and dimensionality reduction is addressed and several PCA-based approaches are explored. Moreover, the energy consumption used by the different word embeddings is investigated. The findings show that some word embedding models can lead to consistently better text classifiers in terms of precision, recall and F1-Score. In particular, for the more challenging classification tasks, BERT combined with PCA stood out with the highest performance metrics. Moreover, our proposed PCA approach of combining word vectors using the first principal component shows clear advantages in performance over the traditional approach of taking the average.

Given the fact that climate change has become one of the most pressing problems in many countries in recent years, specialized research on how to mitigate climate change has been adopted by many countries. Within this discussion, the influence of advanced technologies in achieving carbon neutrality has been discussed. While several studies investigated how AI and Digital innovations could be used to reduce the environmental footprint, the actual influence of AI in reducing CO2 emissions (a proxy measuring carbon footprint) has yet to be investigated. This paper studies the role of advanced technologies in general, and Artificial Intelligence (AI) and ICT use in particular, in advancing carbon neutrality in the United States, between 2021. Secondly, this paper examines how Stock Market Growth, ICT use, Gross Domestic Product (GDP), and Population affect CO2 emissions using the STIRPAT model. After examining stationarity among the variables using a variety of unit root tests, this study concluded that there are no unit root problems across all the variables, with a mixed order of integration. The ARDL bounds test for cointegration revealed that variables in this study have a long-run relationship. Moreover, the estimates revealed from the ARDL model in the short- and long-run indicated that economic growth, stock market capitalization, and population significantly contributed to the carbon emissions in both the short-run and long-run. Conversely, AI and ICT use significantly reduced carbon emissions over both periods. Furthermore, findings were confirmed to be robust using FMOLS, DOLS, and CCR estimations. Furthermore, diagnostic tests indicated the absence of serial correlation, heteroscedasticity, and specification errors and, thus, the model was robust.

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters ($hm^3$) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.

The proliferation of human-AI ecosystems involving human interaction with algorithms, such as assistants and recommenders, raises concerns about large-scale social behaviour. Despite evidence of such phenomena across several contexts, the collective impact of GPS navigation services remains unclear: while beneficial to the user, they can also cause chaos if too many vehicles are driven through the same few roads. Our study employs a simulation framework to assess navigation services' influence on road network usage and CO2 emissions. The results demonstrate a universal pattern of amplified conformity: increasing adoption rates of navigation services cause a reduction of route diversity of mobile travellers and increased concentration of traffic and emissions on fewer roads, thus exacerbating an unequal distribution of negative externalities on selected neighbourhoods. Although navigation services recommendations can help reduce CO2 emissions when their adoption rate is low, these benefits diminish or even disappear when the adoption rate is high and exceeds a certain city- and service-dependent threshold. We summarize these discoveries in a non-linear function that connects the marginal increase of conformity with the marginal reduction in CO2 emissions. Our simulation approach addresses the challenges posed by the complexity of transportation systems and the lack of data and algorithmic transparency.

Alternative routing is crucial to minimize the environmental impact of urban transportation while enhancing road network efficiency and reducing traffic congestion. Existing methods neglect information about road popularity, possibly leading to unintended consequences such as increasing emissions and congestion. This paper introduces Polaris, an alternative routing algorithm that exploits road popularity to optimize traffic distribution and reduce CO2 emissions. Polaris leverages the novel concept of K-road layers, which mitigates the feedback loop effect where redirecting vehicles to less popular roads could increase their popularity in the future. We conduct experiments in three cities to evaluate Polaris against state-of-the-art alternative routing algorithms. Our results demonstrate that Polaris significantly reduces the overuse of highly popular road edges and traversed regulated intersections, showcasing its ability to generate efficient routes and distribute traffic more evenly. Furthermore, Polaris achieves substantial CO2 reductions, outperforming existing alternative routing strategies. Finally, we compare Polaris to an algorithm that coordinates vehicles centrally to distribute them more evenly on the road network. Our findings reveal that Polaris performs comparably well, even with much less information, highlighting its potential as an efficient and sustainable solution for urban traffic management.