The Meteorology is a field where huge amounts of data are generated, mainly collected by sensors at weather stations, where different variables can be measured. Those data have some particularities such as high volume and dimensionality, the frequent existence of missing values in some stations, and the high correlation between collected variables. In this regard, it is crucial to make use of Big Data and Data Mining techniques to deal with those data and extract useful knowledge from them that can be used, for instance, to predict weather phenomena. In this paper, we propose a visual big data system that is designed to deal with high amounts of weather-related data and lets the user analyze those data to perform predictive tasks over the considered variables (temperature and rainfall). The proposed system collects open data and loads them onto a local NoSQL database fusing them at different levels of temporal and spatial aggregation in order to perform a predictive analysis using univariate and multivariate approaches as well as forecasting based on training data from neighbor stations in cases with high rates of missing values. The system has been assessed in terms of usability and predictive performance, obtaining an overall normalized mean squared error value of 0.00013, and an overall directional symmetry value of nearly 0.84. Our system has been rated positively by a group of experts in the area (all aspects of the system except graphic desing were rated 3 or above in a 1-5 scale). The promising preliminary results obtained demonstrate the validity of our system and invite us to keep working on this area.

Anomaly detection is a key task across domains such as industry, healthcare, and cybersecurity. Many real-world anomaly detection problems involve analyzing multiple features over time, making time series analysis a natural approach for such problems. While deep learning models have achieved strong performance in this field, their trend to exhibit high energy consumption limits their deployment in resource-constrained environments such as IoT devices, edge computing platforms, and wearables. To address this challenge, this paper introduces the \textit{Vacuum Spiker algorithm}, a novel Spiking Neural Network-based method for anomaly detection in time series. It incorporates a new detection criterion that relies on global changes in neural activity rather than reconstruction or prediction error. It is trained using Spike Time-Dependent Plasticity in a novel way, intended to induce changes in neural activity when anomalies occur. A new efficient encoding scheme is also proposed, which discretizes the input space into non-overlapping intervals, assigning each to a single neuron. This strategy encodes information with a single spike per time step, improving energy efficiency compared to conventional encoding methods. Experimental results on publicly available datasets show that the proposed algorithm achieves competitive performance while significantly reducing energy consumption, compared to a wide set of deep learning and machine learning baselines. Furthermore, its practical utility is validated in a real-world case study, where the model successfully identifies power curtailment events in a solar inverter. These results highlight its potential for sustainable and efficient anomaly detection.

Drone footage has captured a group of polar bears living inside an abandoned research station on Russia's Kolyuchin Island. Travel blogger, Vadim Makhorov, shared video that shows several bears inside the scattered building, looking through windows and walking around the island. A bear could be seen trying to catch the blogger's drone as it approached. The Kolyuchin weather station was abandoned in the early 1990s, after the collapse of the Soviet Union. Russian Alexey Molchanov breaks his own 2024 world record in one of the most technically challenging freediving events.

Russian freediver Alexey Molchanov plunged 126m (413ft) in a single breath to set a new world record at the AIDA Freediving World Championships in Limassol, Cyprus. He descended deep below the Mediterranean Sea with nothing but a headlight, two fins and a rope as a guide, in a feat considered one of the most technically challenging freedive categories. Mr Molchanov broke his own 2024 world record of 125m, during which he held his breath for a staggering four minutes and 32 seconds. The BBC's Russia editor, Steve Rosenberg, reports from joint manoeuvres by Russia and Belarus, as part of the Zapad 2025 (West 2025) military drills. 'Looks like a toy, but it's real': BBC examines a downed Russian drone Drones like this one were shot down over Polish airspace in the early hours of Wednesday.

Notre-Dame's iconic towers reopen six years after fire The iconic Medieval towers of Notre-Dame Cathedral in Paris have reopened to the public, six years after a massive fire ravaged parts of the historical landmark and forced its closure. The central part of the cathedral was reopened in December 2024, but it has taken longer for Notre-Dame's twin towers to be accessible once again for visitors. A huge restoration project has taken place over the past few years to bring the cathedral back to its former glory after parts of it were substantially damaged during 2019's fire. French President Emmanuel Macron on Friday reopened the newly-restored towers to the public. The BBC's Russia editor, Steve Rosenberg, reports from joint manoeuvres by Russia and Belarus, as part of the Zapad 2025 (West 2025) military drills.

'Looks like a toy, but it's real': BBC examines a downed Russian drone At least three Russian drones were shot down in Poland's airspace during attacks on Ukraine, the Polish prime minister said on Wednesday. The BBC's Sarah Rainsford has been looking at the exact type of Russian drone that flew into Poland, and is proving a massive challenge for Ukraine's territorial defence forces. The BBC's Sarah Rainsford says Sunday's attack caused a huge amount of damage. One of Kyiv's main government buildings was hit in overnight missile and drone strikes by Russia. 'The hit was very hard': Eyewitness in second carriage shares video of crash moment The incident in Lisbon's funicular has left 16 dead and multiple injured.

Ukraine's main government building in Kyiv was hit for the first time since Russia's full-scale invasion of the country on Sunday, officials said. The BBC's Sarah Rainsford visited the scene, where she observed a huge amount of damage. Local media reports suggest a cable came loose along the railway's route, causing it to lose control - a national day of mourning is being observed Actor Julia Roberts makes her Venice Film Festival debut promoting her new movie After The Hunt. The helicopter was attempting to collect water to fight wildfires at the time of the crash. 'Give it a go!': Tips from a top rate tree hugger Top tree hugger Hannah Willow explains why she loves the sport so much.

Nowadays, the volume of network traffic continues to grow, along with the frequency and sophistication of attacks. This scenario highlights the need for solutions capable of continuously adapting, since network behavior is dynamic and changes over time. This work presents an anomaly detection model for network flows using unsupervised machine learning with online learning capabilities. This approach allows the system to dynamically learn the normal behavior of the network and detect deviations without requiring labeled data, which is particularly useful in real-world environments where traffic is constantly changing and labeled data is scarce. The model was implemented using the River library with a One-Class SVM and evaluated on the NF-UNSW-NB15 dataset and its extended version v2, which contain network flows labeled with different attack categories. The results show an accuracy above 98%, a false positive rate below 3.1%, and a recall of 100% in the most advanced version of the dataset. In addition, the low processing time per flow (<0.033 ms) demonstrates the feasibility of the approach for real-time applications.

This paper presents an explainable artificial intelligence (XAI)-based framework for the spectral analysis of cough sounds associated with chronic respiratory diseases, with a particular focus on Chronic Obstructive Pulmonary Disease (COPD). A Convolutional Neural Network (CNN) is trained on time-frequency representations of cough signals, and occlusion maps are used to identify diagnostically relevant regions within the spectrograms. These highlighted areas are subsequently decomposed into five frequency subbands, enabling targeted spectral feature extraction and analysis. The results reveal that spectral patterns differ across subbands and disease groups, uncovering complementary and compensatory trends across the frequency spectrum. Noteworthy, the approach distinguishes COPD from other respiratory conditions, and chronic from non-chronic patient groups, based on interpretable spectral markers. These findings provide insight into the underlying pathophysiological characteristics of cough acoustics and demonstrate the value of frequency-resolved, XAI-enhanced analysis for biomedical signal interpretation and translational respiratory disease diagnostics.

This paper proposes an eXplainable Artificial Intelligence (XAI)-driven methodology to enhance the understanding of cough sound analysis for respiratory disease management. We employ occlusion maps to highlight relevant spectral regions in cough spectrograms processed by a Convolutional Neural Network (CNN). Subsequently, spectral analysis of spectrograms weighted by these occlusion maps reveals significant differences between disease groups, particularly in patients with COPD, where cough patterns appear more variable in the identified spectral regions of interest. This contrasts with the lack of significant differences observed when analyzing raw spectrograms. The proposed approach extracts and analyzes several spectral features, demonstrating the potential of XAI techniques to uncover disease-specific acoustic signatures and improve the diagnostic capabilities of cough sound analysis by providing more interpretable results.