Radio emissions reveal Alfvénic activity and electron acceleration prior to substorm onset

Magnetospheric substorms are among the most dynamic phenomena in the Earth’s magnetosphere, yet their triggering mechanisms remain unclear. Ground-based observations have identified wave-like aurora as precursors to substorms. Here, we report a new precursor feature in space-based observations of auroral kilometric radiation (AKR), marked by the appearance of emissions with slowly frequency-drifting tones (<2 kHz/s) above 100 kHz. Simultaneous multi-instrument obser-vations and statistical analysis suggest that these AKR precursors occur concurrently with wave-like aurora, either manifesting as pseudo-breakup features (with about 5 minutes duration) or as pre-onset activity (about 2 minutes prior to substorm expansion), indicating a common driving mechanism. Analysis of the emissions with frequency-drifting tones suggests that they are linked to moving double-layers driven by dispersive Alfvén waves, consistent with an Alfvénic accelera-tion mechanism for pre-onset aurora. These findings highlight the importance of Alfvénic activity in substorms and suggest that Alfvénic acceleration is not only responsible for optical auroral fea-tures but also for radio emissions, potentially explaining also the ubiquitous frequency-drifting emission features observed at other magnetized planets like Saturn and Jupiter

Productive robots and industrial employment: the role of national innovation systems

In a model with robots, automatable and nonautomatable production, we study robot-labor substitutions and show how they are influenced by a country's “innovation system.” Substitution depends on demand and production elasticities, the country's innovation capabilities, and openness. Making use of World Economic Forum data, we estimate the relationship for 13 countries and find that countries with poor innovation capabilities substitute robots for workers much more than countries with richer innovation capabilities, which might complement them. Innovation capabilities play a bigger role in the high-tech electronics sector than in other manufacturing and play a limited role in nonmanufacturing.</p

Random-key algorithms for optimizing integrated operating room scheduling

Efficient surgery room scheduling is essential for hospital efficiency, patient satisfaction, and resource utilization. This study addresses the challenge as a combinatorial optimization problem that incorporates multi-room scheduling, equipment scheduling, and complex availability constraints for rooms, patients, and surgeons, facilitating rescheduling and enhancing operational flexibility. To solve such a problem, we introduce multiple algorithms based on a Random-Key Optimizer (RKO), coupled with relaxed formulations to compute lower bounds efficiently, rigorously tested on literature and new, real-world-based instances. The RKO approach decouples the problem from the solving algorithms through an encoding/decoding layer, making it possible to use the same solving algorithms to multiple room scheduling problems case studies from multiple hospitals, given the particularities of each place, even other optimization problems. Among the possible RKO algorithms, we design the heuristics Biased Random-Key Genetic Algorithm with Q-Learning, Simulated Annealing, and Iterated Local Search for use within an RKO framework, employing a single decoder function. The proposed heuristics, complemented by the lower-bound formulations, provided optimal gaps for evaluating the effectiveness of the heuristic results. Our results demonstrate significant lower- and upper-bound improvements for the literature instances, notably in proving one optimal result. Our strong statistical analysis shows the effectiveness of our implemented heuristic search mechanisms. Furthermore, the best-proposed heuristic efficiently generates schedules for the newly introduced instances, even in highly constrained scenarios. This research offers valuable insights and practical solutions for improving surgery scheduling processes, delivering tangible benefits to hospitals by optimizing resource allocation, reducing patient wait times, and enhancing overall operational efficiency

Impact of non-digestible carbohydrates and prebiotics on immunity, infections, inflammation and vaccine responses: a systematic review of evidence in healthy humans and a discussion of mechanistic proposals

Prebiotics, particularly non-digestible carbohydrates (NDCs), are increasingly recognized for their role in modulating immune responses in the gut, lungs, and urinary tract. This review systematically evaluates evidence from human studies on the effects of NDCs and prebiotics on immune markers, infection risk and severity, inflammation, and vaccine responses. Prebiotics such as inulin, galactooligosaccharides (GOS), and fructooligosaccharides (FOS) positively influence gut microbiota by promoting beneficial species like Bifidobacteria. They also enhance the production of short-chain fatty acids (SCFAs) like butyrate, which interact with immune cells via G-protein-coupled receptors, inducing anti-inflammatory effects. In addition to microbiota-mediated mechanisms, NDCs and prebiotics may directly affect immune and epithelial cells by interacting with pattern recognition receptors (PRRs), enhancing gut barrier function, and modulating immunity. A systematic review of human studies showed that prebiotics, including GOS, FOS, and 2′-fucosyllactose (2FL), reduced infections and increased IgA in healthy infants, while yeast β-glucan reduced respiratory infection symptoms in healthy adults. Yeast β-glucan and GOS supplementation resulted in improvements in NK cell activity. Some effects on vaccine efficacy were noted in young adults, but the overall impact of NDCs and prebiotics on vaccination and systemic inflammation was inconsistent. Further research is needed to clarify the mechanisms involved and to optimize health applications

The ELECTRA Trial: approach to contemporary challenges in the development and implementation of double-blinded, randomised, controlled clinical trials in low-volume high-complexity surgical oncology

Background: achieving evidence-based practice change in surgery has always been challenging, with many aspects of common clinical practice evolving through lower-level studies that are susceptible to bias and confounding rather than high-quality evidence. This challenge is even more pronounced in the setting of low-volume, high-complexity surgical oncology. Additionally, when the costs of interventions or technologies are high, designing and developing such studies within financially constrained national healthcare systems becomes even more complicated, potentially widening perceived healthcare inequalities between private and publicly funded systems. However, this is precisely the area where a lack of evidence can either hinder the development of significant new clinical advances or lead to the adoption of expensive and ineffective treatments. Here, we describe the novel approaches adopted in the design, development, and implementation of the ELECTRA trial, a randomised, controlled, double-blinded feasibility study with a planned extension to a late-phase trial. Methods: the Cancer Research UK ELECTRA (NCT05877352) trial is a three-armed randomised, controlled clinical trial designed to evaluate the incremental benefit of adding intraoperative electron beam radiotherapy (IOERT) to pelvic exenteration surgery for locally advanced and locally recurrent rectal cancer. ELECTRA is double-blinded, with patients, surgeons, and oncologists unaware of whether IOERT is administered or not. The primary feasibility outcome focuses on the ability to successfully recruit and randomise participants, while the subsequent primary outcome assesses IOERT field local control. Results: we describe the collaborative process involved in developing the trial, including national and international consultations to determine the best study design and the most optimal outcome measures to evaluate. We outline the extensive patient participation and input into the study design. Given the complexity and evolving nature of the field, with no clear international standardisations, we outline the processes used to address internationally agreed definitions, radiological standardisation, surgical learning curves, quality assurance, and pathological standardisation, as well as the broader impact and benefits of these activities. Finally, we describe the novel design utilised to facilitate the involvement of national and international units with varying levels of equipoise regarding IOERT. Conclusions: historically, randomised clinical trials have not been the standard approach for evaluating surgical interventions due to their practical and methodological challenges, particularly in high-complexity, low-volume settings. Despite these difficulties, they remain the gold standard for evidence-based practice. The ELECTRA trial exemplifies a complex, innovative trial design that addresses an unmet need in a specialised area of high-complexity surgery. Using ELECTRA as an example, we highlight the genuine challenges in designing such complex trials and provide recommendations to facilitate the conduct of future well-designed surgical studies.</p

Public and philanthropic research funding, publications and research networks for cancer in the Commonwealth and globally in 2016-2023: a comparative analysis

This Review presents a comprehensive analysis of the amounts and distribution of public and philanthropic global cancer research funding between 2016 and 2023, including patterns of international collaboration and downstream research output, with an emphasis on the Commonwealth. We show that annual investment decreased globally each year, apart from a rise in 2021. Network analysis revealed that grant and publication collaborations between the Commonwealth, the USA, and the EU are facilitated by linkages through a core group of Commonwealth countries, including the UK, Australia, and Canada. There are inequities in research investment and low funding for treatment modalities for many cancers. These inequities also manifest in the central positioning of high-income Commonwealth countries in research collaborations, but also point to opportunities for high-income Commonwealth countries to facilitate linkages with low-income countries and support active cancer research in the USA and the EU. There is an urgent need to review research investment priorities, both within the Commonwealth and globally, to align with population needs and promote collaborative strategies that can build research skills and infrastructure in low-income settings to impact global cancer control. Finite resources should be invested wisely to achieve maximum improvements in mortality and alleviate the cancer burden.</p

Robust and hardware efficient hardware accelerator design for convolutional neural networks

This thesis investigates the integration of approximate computing (AC) techniques into CNN hardware accelerators while addressing security vulnerabilities associated with hardware Trojans (HTs) and backdoor attacks. A comprehensive literature review highlights the need to mitigate these threats, as backdoors attacks can subtly alter classifications, and HTs can cause targeted errors. Meanwhile, the increasing computational demands of CNNs and the limited processing capabilities of embedded devices necessitate lightweight CNN hardware accelerators. AC has emerged as a key approach to enhancing efficiency. However, a major research gap exists in the lack of methodologies for efficiently designing AC-based CNN accelerators and implementing measurements against HTs and backdoor attacks.To bridge this gap, this thesis proposes three methods: Error Matrix-based Error Injection (EMEI), Shuffle and Substitute Defence Mechanism (SSDM), and a selective protection scheme for important processing elements (PEs). EMEI enables fast selection of approximate multipliers for each PE in CNNs, optimising hardware efficiency while maintaining classification accuracy, with a predicted-to-actual accuracy difference of less than 3% on MobileNetV2 using CIFAR-10 and GTSRB. SSDM disrupts HT and backdoor activation through pixel-level shuffling, substitution, and bit-level weight shuffling, reducing activation rates of position-specific, value-specific, pattern-specific, and sequence-specific triggered HTs to below 2%, while detecting neuron-specific HTs within 45 images. Stable patch-based backdoor attack activation rates drop below 5%, while random patch-based and warping-based backdoor attack rates fall below 30%, with additional overhead of less than 0.1%. The selective protection scheme identifies and secures vulnerable PEs. Additionally, two runtime detection methods are introduced: Selective Hardware Redundancy (SHR), which reacts to HT-induced errors within one cycle with &lt;10% overhead, and Selective Hardware and Time Redundancy (SHTR), offering low-overhead (&lt;0.3%) detection within 50–150 cycle

Focal points, beliefs, and distributional preferences: an experimental analysis

This paper experimentally examines the interplay between focal points, beliefs, and distributional preferences. Contrary to common wisdom, there is no detectable evidence that equal splits act as salient focal points in distributional voting contexts. Participants mispredict others’ preferences: they overestimate egalitarianism when Pareto efficient options are available and underestimate it when efficiency gains come at others’ expense. There is a clear correlation between individuals’ preferences and beliefs (false-consensus bias), and participants are more egalitarian when they perceive a low probability of being pivotal (cheap fairman talk). These findings challenge assumptions about focal points and highlight belief-driven behavior in distributional settings

Developing a digital mindfulness-based intervention to improve body image and reduce risk factors for disordered eating: integrating theory, evidence, and the person-based approach

Mindfulness-based interventions (MBIs) show promise in improving body image and reducing risk factors for disordered eating, and their digital adaptation offers scalable dissemination. However, low engagement rates in digital MBIs highlight the need for user-centred development. The person-based approach offers a systematic framework for improving engagement by integrating evidence, theory, and users’ perspectives. This paper describes the application of the person-based approach in developing a digital MBI to reduce risk factors for disordered eating in young people. Intervention development occurred in two iterative phases. In Phase 1, we defined the theoretical context and conducted both a qualitative evidence synthesis and a survey study with a qualitative focus to explore the needs, challenges, and perspectives of the target population. In Phase 2, we developed and refined a prototype based on initial feasibility and acceptability testing through advisory group consultation and think-aloud interviews. These informed the guiding principles and logic model. Our theoretical framework identified the skills of decentred awareness and acceptance, emotion regulation, and self-compassion as key intervention components. Determinants of engagement included negative responses to personal practice, difficulty with habit formation, and social support. Survey findings highlighted the need to address misconceptions about body image, particularly the belief that it refers solely to physical appearance and can be improved through appearance-focused strategies. Feedback from the advisory group helped ensure the intervention was clear, user-friendly, and motivating. This novel integration of theory, evidence, and user-centred design methods provides a replicable model for developing engaging, scalable interventions to reduce disordered eating risk