Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' A Russian air attack has killed two people in Kyiv region's Bilohorodska community, and a drone attack killed another person in central Ukraine's Dnipropetrovsk region, according to local authorities. The deadly attacks came overnight on Wednesday, just hours after a deadly drone attack on a commuter train in northeastern Ukraine's Kharkiv - an incident denounced as "terrorism" by President Volodymyr Zelenskyy, who put the number of people on that train at 200.

A large Russian aerial strike on Ukraine has again left half of Kyiv's residential buildings without heating or power as temperatures across the country continue to hover around -10C. Drones, ballistic and cruise missiles targeted several locations in Ukraine, including Kyiv, Dnipro in the centre and Odesa in the south. Air raid alerts in the capital lasted for most of the night. On Tuesday, sirens rang out again as Russian drones and cruise missiles approached the capital. President Volodymyr Zelensky said a significant number of targets had been intercepted.

Improving energy efficiency in residential buildings is critical to combating climate change and reducing greenhouse gas emissions. Retrofitting existing buildings, which contribute a significant share of energy use, is therefore a key priority, especially in regions with outdated building stock. Artificial Intelligence (AI) and Machine Learning (ML) can automate retrofit decision-making and find retrofit strategies. However, their use faces challenges of data availability, model transparency, and compliance with national and EU AI regulations including the AI act, ethics guidelines and the ALTAI. This paper presents a trustworthy-by-design ML-based decision support framework that recommends energy efficiency strategies for residential buildings using minimal user-accessible inputs. The framework merges Conditional Tabular Generative Adversarial Networks (CTGAN) to augment limited and imbalanced data with a neural network-based multi-label classifier that predicts potential combinations of retrofit actions. To support explanation and trustworthiness, an Explainable AI (XAI) layer using SHapley Additive exPlanations (SHAP) clarifies the rationale behind recommendations and guides feature engineering. Two case studies validate performance and generalization: the first leveraging a well-established, large EPC dataset for England and Wales; the second using a small, imbalanced post-retrofit dataset from Latvia (RETROFIT-LAT). Results show that the framework can handle diverse data conditions and improve performance up to 53% compared to the baseline. Overall, the proposed framework provides a feasible, interpretable, and trustworthy AI system for building retrofit decision support through assured performance, usability, and transparency to aid stakeholders in prioritizing effective energy investments and support regulation-compliant, data-driven innovation in sustainable energy transition.

A Russian drone and missile attack on the Ukrainian capital Kyiv has killed at least one person and injured seven others, city officials say. Early on Saturday morning residential buildings in several districts were hit and loud explosions could be heard across the city. Kyiv's mayor Vitaly Klitschko said a 13-year-old child was among the injured and four people had been taken to hospital. Earlier this week a similar attack on Kyiv killed seven people, Ukrainian officials said. The latest bombardment came as Ukrainian negotiators were preparing for talks with US officials this weekend on an amended US peace plan.

Short-term load forecasting (STLF), which typically ranges from hour-ahead to a few days ahead, plays a multitude of critical roles.

Three people have died and at least 26 others injured in a wave of Russian drone and missile strikes on Kyiv, Ukrainian officials say. Mayor Vitaliy Klitschko described strikes, which caused explosions and fires in residential buildings across the city, as massive. Kyiv's energy infrastructure was also damaged, leaving some buildings in the north-east without heat, he said. Ukraine's air force reported several other regions across the country were also being targeted. Russia's defence ministry said it had downed or intercepted 216 Ukrainian drones that had targeted its industrial facilities and disrupted air travel, according to Reuters news agency.

Short-term load forecasting (STLF), which typically ranges from hour-ahead to a few days ahead, plays a multitude of critical roles.

We present SimCity, a multi-agent framework that leverages LLMs to model an interpretable macroeconomic system with heterogeneous agents and rich interactions. Unlike classical equilibrium models that limit heterogeneity for tractability, or traditional agent-based models (ABMs) that rely on hand-crafted decision rules, SimCity enables flexible, adaptive behavior with transparent natural-language reasoning. Within SimCity, four core agent types (households, firms, a central bank, and a government) deliberate and participate in a frictional labor market, a heterogeneous goods market, and a financial market. Furthermore, a Vision-Language Model (VLM) determines the geographic placement of new firms and renders a mapped virtual city, allowing us to study both macroeconomic regularities and urban expansion dynamics within a unified environment. To evaluate the framework, we compile a checklist of canonical macroeconomic phenomena, including price elasticity of demand, Engel's Law, Okun's Law, the Phillips Curve, and the Beveridge Curve, and show that SimCity naturally reproduces these empirical patterns while remaining robust across simulation runs.

An entire family - a married couple and their two young sons - have been killed in an overnight Russian drone attack in Ukraine's north-eastern Sumy region, local officials have said. Regional head Oleh Hryhorov said a residential building was hit in the village of Chernechchyna. The bodies of the two children, aged four and six, and their parents were later recovered from the wreckage. Ukraine's air force said its units shot down 46 out of 65 Russian drones across the country - but there were 19 direct hits in six locations. Russia's military has not commented.

Population estimation is crucial for various applications, from resource allocation to urban planning. Traditional methods such as surveys and censuses are expensive, time-consuming and also heavily dependent on human resources, requiring significant manpower for data collection and processing. In this study a deep learning solution is proposed to estimate population using high resolution (0.3 m) satellite imagery, Digital Elevation Models (DEM) of 0.5m resolution and vector boundaries. Proposed method combines Convolution Neural Network (CNN) architecture for classification task to classify buildings as residential and non-residential and Artificial Neural Network (ANN) architecture to estimate the population. Approx. 48k building footprints over Gandhinagar urban area are utilized containing both residential and non-residential, with residential categories further used for building-level population estimation. Experimental results on a large-scale dataset demonstrate the effectiveness of our model, achieving an impressive overall F1-score of 0.9936. The proposed system employs advanced geospatial analysis with high spatial resolution to estimate Gandhinagar population at 278,954. By integrating real-time data updates, standardized metrics, and infrastructure planning capabilities, this automated approach addresses critical limitations of conventional census-based methodologies. The framework provides municipalities with a scalable and replicable tool for optimized resource management in rapidly urbanizing cities, showcasing the efficiency of AI-driven geospatial analytics in enhancing data-driven urban governance.