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Guard cell starch and malate metabolism facilitate stomatal opening in response to low CO₂
No full textGuard cells adjust turgor to regulate stomatal aperture, integrating ion transport with metabolic adjustments. The effect of reduced CO₂ on stomatal movement, expected under enhanced photosynthesis, remains largely unexplored.
We exposed Arabidopsis thaliana plants to low CO₂ before light onset and analyzed guard cell starch turnover, malate (Mal) accumulation, and stomatal conductance in wild-type and mutant lines.
Low CO₂ triggered rapid starch degradation and increased Mal levels in guard cells, accelerating stomatal opening. Mutants defective in starch degradation or hexose uptake showed impaired responses.
These findings demonstrate that low CO₂ rewires guard cell metabolism through both internal and external carbon sources, enhancing the responsiveness to light and offering new avenues for stomatal bioengineering
Beyond detection: how students use—and hide—AI in online assessments and what authentic tasks can do about it
No full textAs AI tools such as ChatGPT and CoPilot become increasingly common in higher education, universities must reconsider how assessments are designed, monitored, and supported. This small case study investigates how students use AI in online assessments, whether they disclose such use, and how ethical concerns shape their behaviour. Based on a targeted survey of undergraduate economics students, representing about 18% of the cohort (31/174), we find that only about one-third reported using AI tools, this figure is lower than those reported in several larger surveys. Most reported uses were supportive tasks such as rewording or idea generation. Some students appear to opt out early, suggesting a strategic decision to avoid scrutiny. Fear of penalties is widespread, and exploratory modelling suggests that students with greater ethical concerns may be less likely to use AI at all. At the same time, students express support for guidance and structured regulation. Many favour citation rules and believe AI can be used ethically. Real-world, data-based tasks are widely seen as a way to reduce misuse of AI. The way to tackle the negative learning effects of AI is not by eliminating AI, but by encouraging meaningful engagement. We conclude that students are navigating institutional ambiguity with caution and pragmatism. Overall, our conclusions are preliminary and exploratory: findings are not generalisable, but they point to promising directions for assessment design. Rather than relying on detection and deterrence, universities may achieve better outcomes by aligning assessments with authentic tasks and clear expectations—and by addressing fairness about all students’ access and use of AI
Researching ecosystem dynamics: system dynamics and agent-based modelling as tools for entrepreneurial ecosystem research
No full textEconomic evaluation of digitally supported therapy for people with psychosis who hear distressing voices: the AVATAR2 trial
No full textBackground:
AVATAR therapy, a digitally supported intervention, utilises avatars to promote recovery in people who experience distressing auditory hallucinations. This approach was recently evaluated in a multicentre randomised controlled trial comparing brief (AV-BRF) and extended (AV-EXT) forms of therapy with treatment as usual (TAU). There was evidence for the effectiveness of therapy, particularly for AV-EXT. However, value for money needs to be assessed.
Aims:
To compare separately the cost utility of the brief and extended forms of AVATAR therapy with TAU.
Method:
In a three-arm randomised controlled trial the use of health services was measured, and costs (2021/2022; pounds sterling) calculated from a health and social care perspective over a 28-week follow-up period. Quality-adjusted life years (QALYs; derived from the 5-level version of the EuroQol 5-Dimension questionnaire) were combined with costs.
Results:
AV-BRF resulted in extra costs of £319 (95% CI, −£1558 to £2496), and AV-EXT in lower costs of £1965 (95% CI, −£1912 to £1519), compared with TAU. Over the follow-up, AV-BRF resulted in 0.0159 (95% CI, −0.0103 to 0.0422) and AV-EXT in 0.0173 (95% CI, −0.0049 to 0.0395) more QALYs than TAU. The cost per QALY for AV-BRF compared with TAU was £20 016, while AV-EXT dominated TAU (lower costs and more QALYs).
Conclusions:
Neither version of AVATAR had a substantial impact on QALYs. However, AV-EXT did result in reduced care costs − albeit not statistically significant − and was potentially cost-effective compared with TAU. AV-BRF had an incremental cost-effectiveness ratio that indicated lower potential cost-effectiveness. These findings are uncertain, but could still inform decision-making regarding interventions in this field
A Distributed Framework for Financial Market Trend Prediction Using Hybrid Fuzzy Clustering and Hidden Markov Models
No full textAccurate financial forecasting demands scalable models that adapt to volatility in high-dimensional, temporally dynamic markets. Traditional approaches—such as centralized Hidden Markov Models (HMMs) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity)— struggle with real-time responsiveness and robustness. We propose HMM-DFC, a distributed framework that integrates Fuzzy C-Means (FCM) clustering with HMMs across temporally partitioned nodes to capture evolving market regimes. To enhance adaptability and consistency, we introduce two novel modules: Volatility-Adaptive Transition Refinement (VATR) for sharper regime transitions and Entropy-Based Node Reweighting (EBNR) for stable cross-node synchronization. Extensive evaluation on Standards and Poors 500(S&P500) data shows that HMM-DFC outperforms GARCH, centralized HMMs, and distributed FCM in volatility detection and clustering quality. The framework supports real-time, and scalable forecasting for applications in trading
Balanced exploration and attention-inspired decision making for autonomous driving
Get PDFAutonomous driving has attracted great interest due
to its potential capability in enhancing safety and improving
traffic efficiency. Both model-based and learning-based methods
are widely used in autonomous driving. Out of which, model-based methods rely on existing events in the dataset but are
poor in learning extended situations. As a comparison, the
deep Q-learning network (DQN) has a strong capability in
learning within interactive driving. However, existing DQN faces
challenges in convergence in terms of speed and accuracy,
especially in interactive environments. Furthermore, the poor
convergence causes high risks of collisions and slow driving speed.
Therefore, this paper presents a modified DQN to achieve a lower
number of collisions, higher average driving speed, and faster
convergence during interactive driving. The modified DQN is
developed by introducing a risk-attention mechanism, a balanced
reward function, and a collision-supervised mechanism (RBDQN-CS). The proposed risk-attention mechanism enhances the DQN
to pay attention to high-frequent interactions. The proposed
balanced reward function specifies the weight of the control
strategy to handle the interactions with surrounding human
driven vehicles. The collision-supervised mechanism detects the
collision risks and prevents the collision occurrence during lane-changing. Simulation results demonstrate that the proposed
RBDQN-CS outperforms DQN and other popular baseline DRL
algorithms
A methodological evaluation of app location data extraction and processing for traffic flow applications
No full textEmerging forms of spatial data, such as sensor-based location data collected by mobile apps (MPD), have shown promise in enhancing or replacing traditional analytical methods. MPD provides detailed and timely information at lower costs, making it valuable for urban and transport planning. Multi-source MPD also offers advantages, such as greater representativeness. However, the high volume, variable frequency, and heterogeneity pose significant challenges in data processing and analysis. This study investigates the potential of multi-source MPD to estimate traffic volumes at the street level, focusing on Glasgow city-region. The evaluation covers a range of processing approach combinations. These include three MPD spatial extraction techniques —Simple Buffer (SB), Connected Street Buffer (CB), and Entire Street Buffer (ESB) —to filter relevant vehicular movement data. Additionally, four processing approaches are considered: raw counts (A1), simplified counts with commuter assumptions (A2), detailed geometric analysis (A3), and map matching (A4). The results are compared to manual traffic counts from the Department for Transport (DfT). The findings reveal that raw MPD counts (A1) lead to important biases due to uneven data volume per user. Simplified counts (A2) improve accuracy but still capture non-vehicular activities. Map matching (A4) introduces substantial improvements compared to A1 and A2. However, this approach proves less effective than A3. The geometric-temporal approach (A3) offers the most accurate estimates by incorporating movement inferences. Buffer size and built environment factors influence the methods’ performance, highlighting the need for localised buffers and built environmental controls. This research finds moderate potential for multi-source MPD to supplement traditional traffic systems, providing cost-effective and detailed traffic insights at least from a spatial variability perspective, but not for cross-temporal comparisons. Future studies can extend MPD applications for active travel and extend the analysis to other cities
Government support, populist attitudes and state surveillance in Hungary
Get PDFState surveillance has been extensively investigated in various contexts in relation to the use of technology, external threats and people’s attitudes and characteristics. However, the relationship between political attitudes and the public’s acceptance of state surveillance has been underexplored. Our article addresses this gap in the literature and aims to identify the effects of citizens’ political preferences regarding the acceptance of state surveillance in Hungary. We use individual-level data from a survey conducted in October 2022 on a national-level representative sample. We find that populist attitudes have no effect on the acceptance of surveillance, but also that support for a right-wing government, age and gender have strong explanatory power
In Our Time: Dadaism
No full textMisha Glenny and guests discuss the provocative artistic phenomenon that first startled audiences in 1916 in Zurich. There, at the Cabaret Voltaire at the Holländische Meierei on the Spiegelgasse, Emmy Hennings and Hugo Ball and others gathered on a small stage, sometimes dressed in cardboard, often performing nonsense poems. This was the start of Dada, a spirit more than a movement which spread to other cities in Europe during the war. In part the Dadas (as they called themselves) were protesting against the inevitability of constant wars on the continent and in part this was an artistic experiment around the absurd; they were creating poems, songs, costumes and art that made no obvious sense, just as the war around them made no sense to the artists, designers and poets at the Cabaret Voltaire.
With
Dawn Ades
Emeritus Professor of Art History and Theory at the University of Essex
Ruth Hemus
Professor of French and Visual Culture at Royal Holloway, University of London
And
Stephen Forcer
Professor of French at the University of Glasgow
Produced by Martha Owen
In Our Time is a BBC Studios Production
Spanning history, religion, culture, science and philosophy, In Our Time from BBC Radio 4 is essential listening for the intellectually curious. In each episode, host Misha Glenny and expert guests explore the characters, events and discoveries that have shaped our world
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