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“You should be able to eat that meal and feel like someone cares”: community food carers, good food, and the emergence of food-aid mutualism during Covid-19
Full text linkAusterity policies in the UK have fostered a ‘foodbank society’ by reducing state support and normalising reliance on charitable aid and poor-quality food. As a result, individuals’ physical, emotional, and mental relationships with food are being reshaped. This paper draws upon on geographies of mutual aid and care and the visceral framework in food geographies, to examine the improvised community foodwork that emerged in Manchester during the Covid-19 pandemic. Using qualitative interviews and participatory foodwork in a local food bank, I show how community food workers reintroduced good food into emergency food provision by attending to dignity, agency and sensory pleasure rather than prioritising scarcity and functionality. At the same time, I found that these practices also supported the food workers own emotional wellbeing during the uncertainty surrounding lockdown events. Whilst this foodwork forged reciprocal affective relations, it also sits in an ambivalent political space shaped by welfare retreat, is vulnerable to neoliberal co-optation and is also over-reliant on unpaid gendered and racialised labour. I therefore conceptualise this convergence of material and affective care as food mutualism, a form of reciprocal nourishment that emerges through foodwork and both challenges and reproduces the inequalities produced by austerity. By engaging with both the political stakes of mutualism and viscerality this work provides insight into how community-based food aid might be reimagined beyond the neoliberal foodbank model in ways that centre dignity, reciprocity, and sensory pleasure
Anatomy of moist heatwaves in India during the summer monsoon season
No full textMoist heat impairs the human body’s ability to cool through sweat-based evaporative cooling, posing a serious health risk. In India, this risk is especially acute, since the Indian summer monsoon (ISM) brings abundant moisture, and socio-economic conditions significantly increase the exposure and vulnerability to moist heat. However, there is a limited understanding of the characteristics and large-scale drivers of moist heatwaves during the ISM. This study uses the ERA5 reanalysis to analyse moist heatwaves and their relationship with active and break periods of the ISM during 1940– 2023. An empirical orthogonal function analysis of daily maximum wet-bulb temperature (Tw) anomalies reveals that the first two principal components (PCs) explain key patterns of variability of moist heatwaves, with PC1 controlling their occurrence and PC2 controlling their spatial extent. Whilst breaks in the monsoon favour moist heatwaves in eastern and peninsular India, active rainfall events, corresponding to phases 5–7 of the Boreal Summer Intraseasonal Oscillation, favour moist heatwaves in northern and northwestern India. Specific humidity plays a larger role than dry-bulb temperature in controlling Tw variability in India. The results of this study reveal important characteristics of moist heatwaves during the ISM and offer potential for developing forecasting tools, which could ultimately benefit stakeholders in India
RECYCLE-REUSE-REDUCE: developing sustainable food packaging toolkits for school-aged children in Poland and Spain – a pilot study
Full text linkEngaging future generations in sustainable initiatives is fundamental; accordingly, children need relevant education from an early age. This paper investigates the role of repeated exposure to interactive, creative and practical activities on subsequent awareness of sustainable food-based practices in two countries, Poland and Spain. Children (n = 89; 9–12 years) completed a series of activities centred around recycle, reuse and reduce “The 3Rs concept” coupled with surveys to quantify changes in knowledge pre and post activities. Overall, it was evident that children had relatively high environmental awareness and cited key sources of information as school and television. The activities were effective at increasing engagement with sustainable topics and practices. Sustainable behaviours increased: at school grounds mainly with waste disposal (sorting and recycling), daily practices outside school tapped into general environmental behaviours (saving resources, reducing waste), and disposal practices (sorting). Next steps will involve developing children-centric toolkits, measuring impact in different settings and encouraging uptake of everyday sustainable practices in children
Salmonella relies on siderophore exploitation at low pH
Full text linkSalmonella enterica, a prominent enteric pathogen, employs sophisticated iron acquisition mechanisms to overcome host-imposed iron limitation, notably through the production and uptake of siderophores—small, high-affinity iron-chelating compounds that scavenge iron from the host environment. In this study, we investigate how environmental pH influences Salmonella’s preference for its endogenous siderophores versus exogenous siderophores within the physiological range of the gastrointestinal tract. Through competition assays, gene expression analysis, and siderophore quantification, we demonstrate that Salmonella increasingly relies on exogenous siderophores under acidic conditions. This shift is attributed to reduced production of its endogenous siderophores, enterobactin and salmochelin. Deletion of the sigma factor RpoS enhances iron acquisition through increased endogenous siderophore production at low pH, suggesting a role in iron homeostasis regulation. Our findings reveal a pH-dependent difference in Salmonella’s iron acquisition strategy, highlighting the pathogen’s versatility in nutrient acquisition across varying gastrointestinal conditions. This research provides insights into Salmonella’s pathogenicity and may inform the development of targeted interventions for Salmonella infections
Appraising cascading systemic risks, ‘watchpoints’, and interventions –methodological reflections
Full text linkNon-technical summary: Continued global environmental degradation generates risks to human health, for example, through air pollution, disease, and food insecurity. This study focuses on these three types of health impact and explores what drives these risks. The risks can arise from diverse causes including political, economic, social, technological, legal/regulatory, and environmental factors. We assembled diverse experts to work together to produce ‘system maps’ for how risks arise, identifying monitoring ‘watchpoints’ to help track risks and interventions that can help prevent them materialising. We critically appraise this pilot methodology, in order to improve our capacity to understand and act to protect human health.
Technical summary: Systemic risks arise through a process of contagion across political, economic, social, technological, legal/regulatory, and environmental systems. The highly complex nature of these risks prevents probabilistic assessment as is carried out for more conventional risks. This study critically explores a new approach based on participatory systems mapping with experts from diverse backgrounds helping to appraise these risks and identify data and monitoring ‘watchpoints’ to track their progress. We focus on three case studies: air quality, biosecurity, and food security. We assembled 36 experts selected in a stratified way to maximise cognitive diversity, plus 14 members of the interdisciplinary project team. Across 7 workshops, we identified 39 ‘risk cascades’, defined as pathways by which systemic risk can have negative impacts on human health, and we identified 681 watchpoints and interventions. We identify a broad range of interventions to reduce risk, exploring systems approaches to help prioritise these interventions; for example, understanding co-benefits in terms of reducing multiple different types of risk, as well as trade-offs. In this paper, we take a reflective approach, critically discussing constraints and refinements to our pilot methodology, in order to enhance capacity to appraise and act on systemic risks.
Social media summary: How can we act on the risks from air pollution, disease, and food insecurity? Insights from a new systemic risk assessment methodology
A high-performance Sketch with Dynamic Memory Allocation for priority-oriented data stream processing
Full text linkSketch is widely used in many traffic estimation tasks due to its good balance among accuracy, speed, and memory usage. In scenarios with priority flows, priority-aware sketch, as an emerging method, provides differentiated detection accuracy for flows of different priorities, optimizing resource allocation and improving the detection accuracy of high-priority flows. However, existing priority-aware sketches methods struggle to effectively handle the dynamic changes in flow priority distribution in real-world detection environments, leading to wasted or insufficient storage space. To address this issue, this paper proposes a new priority-aware sketch with Dynamic Memory Allocation called DMA-Sketch. It dynamically adjusts the detection framework based on flow priority distribution information and adaptively allocates appropriate memory space to each storage region. The experimental results show that DMA-Sketch improves the overall priority accuracy, high-priority accuracy and throughput by up to 1.33x, 16.39x and 1.88x, respectively, under the scenarios with changing flow priority distribution over the state-of-the-art schemes
Using large language model based AI suspects to train strategic use of evidence: preliminary evidence of transfer to mock suspect interviews
No full textObjectives: The Strategic Use of Evidence (SUE) is a technique that aims to improve the ability to differentiate between liars and truth-tellers. However, while theoretical training provides guidance on interview techniques, it lacks opportunities for practical application. Hypotheses:
We developed two Large Language Model driven AI Suspects with whom participants could simulate interviews and hypothesized that these simulations would enhance the transfer of training to later interactions with Human Mock Suspects. Method: The study included 156 Chinese laypersons (78 Interviewers and 78 Human Mock Suspects). The two AI suspects followed response rules representing simplified and prototypical examples of liars’ and truth-tellers’ behaviors under the SUE model. Interviewers were randomly allocated to one of three types of training: (a) Instruction & AI Exercise, (b) Instruction, and (c) Control. After the training, the participants interacted with either a lying or truthful Human Mock Suspect. Results:
Receiving interventions made Interviewers use Evidence Framing Matrix (EFM: an important tactic within the SUE framework) more frequently, thereby eliciting more inconsistencies between the lying Human Mock Suspects’ statements and the evidence (i.e., evidence-statement inconsistencies) as well as more inconsistencies within their own statements (i.e., within-
statement inconsistencies). Both Instruction and Instruction & AI Exercise groups used evidence-statement (in)consistencies more to make their judgments about whether Human Mock Suspects were lying or truthful compared to those in the Control group. Additionally, the Instruction & AI Exercise group was better at accurately judging whether the Human Mock Suspects were lying
or truthful compared to the Control group. Conclusions: Overall, this study provided preliminary evidence that simulated SUE training with AI Suspects transferred to interactions with Human Mock Suspects in a controllable experimental setting but that the advantage over instruction-only
was not particularly robust
Height-based biomass models differ for naturally regenerated and planted young trees
Full text linkThis study investigated biomass allocation in young stands of European beech (Fagus sylvatica L.) and Norway spruce (Picea abies Karst L.) across 31 forest sites in the Western Carpathians, Slovakia. A total of 541 trees aged 2–10 years, originating from natural regeneration and planting, were destructively sampled to quantify biomass in four components: foliage, branches, stems, and roots. Generalised Non-Linear Least Squares (GNLS) models with a weighing variance function outperformed log-transformed Seemingly Unrelated Regression (SUR) models in terms of accuracy and robustness, especially for foliage and branch biomass. When using height as the predictor, SUR models tended to underestimate biomass in planted beech, leading to notable underprediction of aboveground and total biomass. Biomass allocation patterns varied significantly by species and regeneration origin. Using a non-linear system of equations and Component Ratio Modelling, we found out that planted spruce displayed low variability and a consistent dominance of needle biomass, while naturally regenerated beech showed greater variability and a higher proportion of stem biomass, reflecting stronger competition-driven vertical growth. Interspecific differences in total biomass were more pronounced when using tree height, with spruce generally exhibiting greater biomass than beech at equivalent heights. Overall, stem base diameter marginally outperformed tree height as a predictor of biomass. However, tree height-based models showed strong performance and are particularly suitable for integration with remote sensing applications. These findings can directly support forest managers and modellers in comparing regeneration methods and biomass estimation approaches for early-stage stand development, carbon accounting, and remote sensing calibration