Angeliki Stogia has been selected as the Labour Party candidate for the upcoming Gorton and Denton by-election. The Manchester councillor was chosen to represent the party after Greater Manchester mayor Andy Burnham was denied permission to enter the contest a week ago. The by-election on 26 February in the Greater Manchester constituency was prompted by the resignation of former MP Andrew Gwynne on health grounds. Stogia said she was thrilled and excited as a proud Mancunian woman to start campaigning in the constituency. She said she was so looking forward to going out on the doorstep and winning this for Labour.

'I spoke to ChatGPT 8 times a day' - Gen Z's loneliness'crisis' Working from home after years spent alone over Covid lockdowns, 23-year-old Paisley said he began to feel trapped, and felt only AI could help him. I lost the ability to socialise, he said, and like many in Gen Z, he turned to AI for company. At one point, I was talking to ChatGPT six, seven, eight times a day about my problems, I just couldn't get away from it, it was a dangerous slope. He shared his experience of loneliness with 22-year-old documentary maker Sam Tullen, who told the BBC what Paisley was going through was part of a wider Gen Z loneliness crisis. Gen Z, a term used for those born between 1997 and 2012, often referred to as the first'digital native' generation.

Objective: Large language models (LLMs) are attracting increasing interest in healthcare. This commentary evaluates the potential of LLMs to improve clinical prediction models (CPMs) for diagnostic and prognostic tasks, with a focus on their ability to process longitudinal electronic health record (EHR) data. Findings: LLMs show promise in handling multimodal and longitudinal EHR data and can support multi-outcome predictions for diverse health conditions. However, methodological, validation, infrastructural, and regulatory chal- lenges remain. These include inadequate methods for time-to-event modelling, poor calibration of predictions, limited external validation, and bias affecting underrepresented groups. High infrastructure costs and the absence of clear regulatory frameworks further prevent adoption. Implications: Further work and interdisciplinary collaboration are needed to support equitable and effective integra- tion into the clinical prediction. Developing temporally aware, fair, and explainable models should be a priority focus for transforming clinical prediction workflow.

Survivors of childhood cancer need lifelong monitoring for side effects from radiotherapy. However, longitudinal data from routine monitoring is often infrequently and irregularly sampled, and subject to inaccuracies. Due to this, measurements are often studied in isolation, or simple relationships (e.g., linear) are used to impute missing timepoints. In this study, we investigated the potential role of Gaussian Processes (GP) modelling to make population-based and individual predictions, using insulin-like growth factor 1 (IGF-1) measurements as a test case. With training data of 23 patients with a median (range) of 4 (1-16) timepoints we identified a trend within the range of literature reported values. In addition, with 8 test cases, individual predictions were made with an average root mean squared error of 31.9 (10.1 - 62.3) ng/ml and 27.4 (0.02 - 66.1) ng/ml for two approaches. GP modelling may overcome limitations of routine longitudinal data and facilitate analysis of late effects of radiotherapy.

'This is a matter of national security.' An artist's rendering of the spacecraft in the SWIFT constellation stationed in a triangular pyramid formation between the sun and Earth. A solar sail allows the spacecraft at the pyramid's tip to hold station without conventional fuel. Breakthroughs, discoveries, and DIY tips sent every weekday. Just like Earth's severe thunderstorms, solar storms can cause their own kinds of havoc.

Minimally curated tabular data often contain representational inconsistencies across heterogeneous sources, and are accompanied by sparse metadata. Working with such data is intimidating. While prior work has advanced dataset discovery and exploration, schema inference remains difficult when metadata are limited. We present SI-LLM (Schema Inference using Large Language Models), which infers a concise conceptual schema for tabular data using only column headers and cell values. The inferred schema comprises hierarchical entity types, attributes, and inter-type relationships. In extensive evaluation on two datasets from web tables and open data, SI-LLM achieves promising end-to-end results, as well as better or comparable results to state-of-the-art methods at each step. All source code, full prompts, and datasets of SI-LLM are available at https://github.com/PierreWoL/SILLM.

This paper examines the impact of Generative Artificial Intelligence (GenAI) on scientific practices, conducting a qualitative review of selected literature to explore its applications, benefits, and challenges. The review draws on the OpenAlex publication database, using a Boolean search approach to identify scientific literature related to GenAI (including large language models and ChatGPT). Thirty-nine highly cited papers and commentaries are reviewed and qualitatively coded. Results are categorized by GenAI applications in science, scientific writing, medical practice, and education and training. The analysis finds that while there is a rapid adoption of GenAI in science and science practice, its long-term implications remain unclear, with ongoing uncertainties about its use and governance. The study provides early insights into GenAI's growing role in science and identifies questions for future research in this evolving field.

This study employs Long Short-Term Memory (LSTM) networks to forecast key performance indicators (KPIs), Occupancy (OCC), Average Daily Rate (ADR), and Revenue per Available Room (RevPAR), across five major cities: Manchester, Amsterdam, Dubai, Bangkok, and Mumbai. The cities were selected for their diverse economic profiles and hospitality dynamics. Monthly data from 2018 to 2025 were used, with 80% for training and 20% for testing. Advanced time series decomposition and machine learning techniques enabled accurate forecasting and trend identification. Results show that Manchester and Mumbai exhibited the highest predictive accuracy, reflecting stable demand patterns, while Dubai and Bangkok demonstrated higher variability due to seasonal and event-driven influences. The findings validate the effectiveness of LSTM models for urban hospitality forecasting and provide a comparative framework for data-driven decision-making. The models generalisability across global cities highlights its potential utility for tourism stakeholders and urban planners.

-- We study parameterizations of stabilizing nonlinear policies for learning-based control. We propose a structure based on a nonlinear version of the Y oula-Ku ˇ cera parameterization combined with robust neural networks such as the recurrent equilibrium network (REN). The resulting parameterizations are unconstrained, and hence can be searched over with first-order optimization methods, while always ensuring closed-loop stability by construction. We study the combination of (a) nonlinear dynamics, (b) partial observation, and (c) incremental closed-loop stability requirements (contraction and Lipschitzness). We find that with any two of these three difficulties, a contracting and Lipschitz Y oula parameter always leads to contracting and Lipschitz closed loops. However, if all three hold, then incremental stability can be lost with exogenous disturbances. Instead, a weaker condition is maintained, which we call d-tube contraction and Lipschitzness. We further obtain converse results showing that the proposed pa-rameterization covers all contracting and Lipschitz closed loops for certain classes of nonlinear systems. Numerical experiments illustrate the utility of our parameterization when learning controllers with built-in stability certificates for: (i) "economic" rewards without stabilizing effects; (ii) short training horizons; and (iii) uncertain systems. Deep reinforcement learning (RL) is an emerging technology for general-purpose nonlinear control design via simulation. It has been successfully applied in many complex domains ranging from strategy games [1] to robotics [2], [3] to nuclear fusion [4]. The standard approach is to minimize empirical estimates of an expected cost over repeated episodes with random disturbances, typically using deep neural networks for black-box policy parameterizations [5].

Kid Rock told Fox News Digital that he doesn't necessarily set out to write patriotic music, but "the message of patriotism in my music has just always been there." Rage Against the Machine guitarist Tom Morello had some choice words for President Donald Trump at a concert on Sunday. Rolling Stone reported that during his performance at the Boston Calling Music Festival, the famous musician unloaded on Trump in response to the president's recent spat with classic rock legend Bruce Springsteen. "Bruce is going after Trump because Bruce, his whole life, he's been about truth, justice, democracy, equality," Morello said onstage, adding, "And Trump is mad at him because Bruce draws a bigger audience. The feud between Trump and Springsteen began nearly two weeks ago when the artist accused the president of treason during a concert in Manchester, England. "The mighty E Street Band is here tonight to call upon the righteous power of art, of music, of rock and roll in dangerous times.