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Evaluating biological realism in ecological modelling: application of a novel framework to compare mechanistic and process-based earthworm and wild pollinator population models
Full text linkEcological models can support land management decisions and optimisation schemes that need to account for invertebrate population responses at the field to landscape level. However, models that incorporate greater biological detail (e.g. individual-level physiological and behavioural responses) often become computationally intractable at larger spatial extents. Such trade-offs in model development lead to ad hoc model design for different species and management questions, hindering generalisable insights needed to advance predictive ecological models for decision support. To facilitate model comparison, we developed and applied a novel approach to quantify the biological realism of models for two functionally important invertebrate groups commonly targeted by management interventions. Mechanistic and process-based population models for earthworms (n = 23) and wild pollinators (n = 24) were identified through a structured review. We find that earthworm models are predominantly non-spatial or micro-scale (<10 m extent) and often incorporate detailed physiological mechanisms. Pollinator models frequently simulate landscape-scale scenarios (≥1 km extent) and typically rely on aggregated processes to predict population dynamics or crop visitation rates, although some include detailed individual-level movement behaviours. Species- and scale-specific model structures highlight the need for greater integration of physiological and behavioural mechanisms across broader spatial extents. We recommend systematic strategies to build on the progress made by existing models, aiming to resolve the trade-off between realism and tractability for more informed population predictions at management-relevant spatial scales. Our framework complements existing efforts towards greater transparency in model development, communication, and application for robust environmental decision support
Quantifying and monetising externalities in Kenya's green bean value chain: implications for stakeholder and policy actions
Full text linkGrowing international demand for fresh green beans is driving producers in Kenya to expand and intensify crop production for export, creating negative environmental, health and social externalities (hidden costs). However, empirical evidence on the magnitude of these externalities remains limited. Estimating these externalities to reveal their magnitude could encourage stakeholder and policy actions that ensure a more environmentally sustainable, health-protective and socially equitable value chain. This study quantified and monetised negative environmental, health and social externalities in Kenya's green bean value chain. True cost accounting approaches, including life cycle assessment, disability-adjusted life years, the True Price methodology and the value of statistical life years, were used to analyse data from secondary sources. The total hidden costs were estimated at 124.03 million USD (range 115.93–132.20), at least twice the 53.92 million USD market value of green beans and almost three times the export value (42.15 million USD). Environmental externalities accounted for 86.87 million USD (range 79.16–94.65), driven mainly by scarce blue water use and greenhouse gas emissions. Health externalities accounted for 0.97 million USD (range 0.58–1.36), primarily from pesticide exposure. Social externalities (36.20 million USD) reflected a large living income gap among smallholder farming households and the presence of child labour. In conclusion, Kenya's green bean value chain creates substantial negative environmental, health and social externalities. There is a need for stakeholder and policy actions to internalise externalities in the value chain. The findings can guide stakeholders and policymakers in developing and implementing strategies to reduce externalities
With whom to ally? Alliance strategy for EV battery supplier considering echelon utilization and disassembly recycling
No full textThe rapid expansion of the electric vehicle (EV) industry has heightened the need for sustainable and efficient closed-loop supply chains (CLSC) that can simultaneously improve economic returns and mitigate environmental impacts. To address this challenge, this study develops a game-theoretic model from the perspective of the power battery supplier and examines four inter-firm alliance modes: Non-alliance (N), supplier-manufacturer alliance (SM), supplier-recycler alliance (SR), and comprehensive alliance (SMR). The results reveal that (1) in the forward supply chain, suppliers under the SM and SMR modes consistently achieve higher battery capacity and EV sales. In the reverse supply chain, suppliers in alliance modes (SM, SR, SMR) are able to pay lower recycling prices while securing higher recycling quantities. (2) When recycling competition is weak, alliance with the manufacturer improves economic performance, whereas that with the recycler enhances environmental outcomes; however, the two benefits cannot be achieved simultaneously. By contrast, under intense recycling competition, forming a comprehensive alliance allows suppliers to improve both environmental and economic performance. (3) When extending the analysis to include suppliers’ investment in echelon utilization technology innovation, increased recycling competition intensity leads to a decline in the supplier’s echelon utilization performance, thereby amplifying the advantage of the comprehensive alliance
Course of well-being and mental health in Switzerland during the COVID-19 pandemic: results of a national survey within the framework of the COH-FIT study
Full text linkIntroduction: The COVID-19 pandemic significantly impacted the mental health of the Swiss population.
Methods: This study analyzed data from the Collaborative Outcome study on Health and Functioning during Infection Times (COH-FIT) across three pandemic waves: T1 (April–June 2020), T2 (July–December 2020) or T3 (January–June 2021). Each participant participated only once, during one of these three waves. Participants reported their subjective well-being and mental health status for the two weeks prior to the pandemic (pre-pandemic baseline) and during their respective pandemic wave. Subjective well-being was assessed using the World Health Organization Well-Being Index (WHO-5) from 4,037 participants, while mental health was measured via the P-score, completed by 3,375 participants. The WHO-5 ranges from 0 to 100, with higher scores indicating better well-being, while the P-score also ranges from 0 to 100 whereas higher scores represent greater levels of perceived burden across five domains of mental health. Pre- and intra-pandemic differences were analyzed using Wilcoxon tests and ANOVA, with subgroup analyses across seven Swiss regions utilizing the Kruskal-Wallis test.
Results: Participants had a mean age of 45.6 years (61.9% female). Results showed a substantial decline in well-being during the pandemic, with average WHO-5 scores decreasing from 75.3 pre-pandemic, to 66.5 during the first wave, 69.1 during the second, and 65.1 during the third, representing relative reductions of 11.7%, 8.2%, and 13.5%. The percentage of participants at risk for depression (WHO-5 <50) peaked during the third wave at 19.8%, up from 10.0% pre-pandemic. Mental health burden, as measured by the P-score, increased significantly during the first wave (from 20.6 to 27.3, +32.5%), and remained elevated across the two subsequent waves, with no significant recovery observed. Wilcoxon tests indicated significant differences between pre-pandemic and intra-pandemic WHO-5 and P-scores, with the largest effect sizes during the third wave (r = 0.652 for WHO-5; r = 0.487 for P-score). ANOVA showed significant intra-pandemic differences in WHO-5 across waves (p < 0.001), with improvements noted in the second wave. However, no intra-pandemic differences in P-scores were found (p = 0.298). Regional analyses revealed that Ticino, the Lake Geneva region, and Northwestern Switzerland experienced the most pronounced declines in well-being and increases in mental health burden. In contrast, Espace Mittelland and Eastern Switzerland experienced a less severe impact.
Discussion: Overall, these findings highlight the considerable and lasting impact of COVID-19 on mental health in Switzerland, emphasizing the need for targeted interventions, particularly in the most affected regions
Natural antimicrobial activity of nettle (Urtica dioica L.) leaf extract for shelf-life extension of mashed potatoes
Full text linkThe growing demand for minimally processed clean-label foods has intensified interest in natural antimicrobials as alternatives to synthetic preservatives. However, very little is known about the antimicrobial potential of several wild edible plants when incorporated into food matrices. This study evaluated the antimicrobial activity of nettle (Urtica dioica L.) leaf extract and as a clean-label preservative for extending the shelf life of fresh mashed potatoes. The extract exhibited strong antioxidant activity, with DPPH, ABTS, and FRAP values of 21.96 ± 0.76 μmol Trolox/mL, 17.51 ± 0.90 μmol Trolox/mL, and 5.93 ± 0.65Fe(II)/g, respectively. In vitro antimicrobial testing confirmed broad-spectrum activity, with minimum inhibitory and bactericidal concentrations indicating pronounced susceptibility of Gram-positive bacteria (Staphylococcus aureus, Bacillus cereus, Listeria monocytogenes) and notable effects on Gram-negative pathogens (E. coli, Salmonella enterica serovar Typhimurium).Cytotoxicity assessment using L929 fibroblast cells showed the extract was non-toxic at concentrations effective for antimicrobial application. When incorporated into mashed potatoes at 0.5%–2.0% (w/v), nettle extract achieved preservative effects comparable to 0.025% commercial nisin. Treated samples exhibited significantly delayed increases in total viable counts, psychrotrophs, Enterobacteriaceae, B. cereus, S. aureus, P. aeruginosa, and spoilage fungi during storage at 4°C and 25°C. Electronic tongue analysis differentiated treatment groups, revealing mild bitterness and astringency at increasing nettle leaf extract incorporation, but these effects were less detrimental than spoilage-related off-flavors in untreated controls. Overall, nettle leaf extract provides combined antimicrobial and antioxidant functionality, enhances microbial stability, and maintains acceptable sensory quality, supporting its potential as a natural alternative to synthetic preservatives in ready-to-eat mashed potato products
Implementing restorative approaches in the further education sector in England: successes, constraints, and limitations
Full text linkInstitutions in the education sector regularly encounter behavioural challenges; therefore, they have adopted restorative approaches (RA) to better manage and address such situations, creating a safer environment and helping the perpetrator reflect on the impact of their behaviour. In the UK, research on RA in education focuses on primary and secondary schools but is limited and underdeveloped in the Further Education (FE) sector. Based on data collected over 14 months, this study explores staff experiences of successes, constraints, and limitations for a successful RA policy implementation. This study adopted a qualitative method design framework and found successes and barriers to effective and successful RA implementation in the FE sector. Findings indicate that both top-down and bottom-up approaches to implementation are pivotal. RA policy is interrupted or reversed due to mergers or changes in Senior Leadership Teams (SLT). A lack of regular training and professional development programmes on RA impacts the delivery of the concept. The findings from this study will be relevant to FE institutions, their staff, and the Department for Education when planning to implement an RA policy
Reduced order modelling of air puff test for corneal material characterisation
Full text linkModels of the fluid-structure interaction (FSI) model for the air puff test were analysed. Using Abaqus, the air puff test is applied to eyes with varying biomechanical parameters, such as material properties, corneal thickness, and radius. A reduced order model of the air puff (a turbulent impinging jet) has been acquired to decrease simulation time from 48 hours for the FSI model to approximately 12 minutes for the finite element analysis (FEA) model alone. To further accelerate simulations and improve model accuracy, Physics-Informed Neural Networks (PINNs) will be integrated with the reduced-order model. This hybrid approach will help expand the model to a larger dataset, enhancing intraocular pressure (IOP) estimation accuracy and the corneal material properties algorithm through inverse FEA. Additionally, a neural network (NN) framework with embedded Gaussian-modulated waveforms is proposed to model the pressure and deformation distributions on the corneal surface as functions of spatial and temporal parameters. By learning the relationship between corneal biomechanical inputs such as Corneal Central Thickness (CCT), Intraocular Pressure (IOP), and baseline properties (µ), and the governing coefficients of pressure and deformation, the network accurately reconstructs the result that matches well with the high-fidelity CFD data. This approach can quickly capture the distribution of pressure and deformation. It can also provide insights into the distinct spatial and temporal dynamics of pressure and deformation, giving a more comprehensive understanding of fluid-structure interaction phenomena in the air puff test
A novel attention-enhanced hybrid deep learning approach for malaria diagnosis in microscopic cell images
Full text linkBackground: Malaria remains a major global public health challenge, transmitted through the bites of infected female mosquitoes. Despite progress in prevention and treatment, the disease continues to cause significant morbidity and mortality, especially among children and pregnant women. Conventional diagnostic methods relying on microscopic examination are time-consuming, dependent on skilled personnel, and prone to human error. Existing deep learning models for malaria detection often show limited performance and overfitting.
Methods: A hybrid deep learning model was developed by combining EfficientNetB0 with a custom convolutional neural network (CNN) enhanced by attention mechanisms to classify malaria-infected and uninfected red blood cell images. EfficientNetB0 provided pre-trained global feature extraction, while the custom CNN captured domain-specific features. The model was trained and evaluated using a publicly available malaria dataset.
Findings: The proposed hybrid model achieved a classification accuracy of 96.53%, precision of 94.80%, recall of 98.22%, F1-score of 95.95%, and an AUC of 99.12%. Attention maps offered interpretability by highlighting biologically relevant regions within cell images. Comparative experiments showed that the hybrid model outperformed standalone EfficientNetB0 and CNN architectures.
Interpretation: Integrating global and domain-specific feature representations significantly improves malaria image classification performance. The proposed hybrid model demonstrates strong potential for use in automated malaria diagnosis systems, supporting early detection and timely treatment
Decolonising bias in organisational systems: a machine learning approach to equity, power, and algorithmic justice
No full textThis chapter examines how algorithmic systems can reproduce and exacerbate structural inequities along gender and racial lines, using the Adult Income dataset as a testbed for comparative analysis across four models: Logistic Regression, XGBoost, Explainable Boosting Machines (EBM), and Adversarial Debiasing Networks. Empirical evaluation revealed substantial disparities in unmitigated models, with disparate impact ratios falling as low as 0.68 for women and non-white individuals. Crucially, this study embeds technical findings within a decolonial theoretical framework, arguing that fairness cannot be reduced to statistical parity. Instead, it must be understood as a historically situated, epistemically accountable, and relationally constructed concept. The research challenges dominant narratives of algorithmic neutrality by foregrounding the colonial legacies and institutional hierarchies that inform both data practices and model design. By bridging machine learning evaluation with critical social theory, this research advances a reflexive, justice-oriented approach to algorithmic governance in organisations. It offers a framework for rethinking fairness not simply as a computational objective, but as a moral and organisational commitment grounded in equity, participatory design, and the inclusion of marginalised knowledges
Irrelevant task difficulty modulates the emergence of task conflict
Full text linkIn cognitive control tasks, participants are typically instructed to respond to a task-relevant dimension of a stimulus while ignoring the task-irrelevant one(s). In such experiments, task conflict reflects the additional effort associated with performing two tasks, such as identifying the color while reading the word in the color-word Stroop task. Task conflict is commonly inferred by comparing conditions that consist of two tasks (e.g., congruent and incongruent trials) with conditions that only consist of one task (meaningless non-word neutral trials). In three experiments, we used a color-digit Stroop task that varied in the difficulty of the irrelevant dimension of the stimuli, with these differences explicitly examined in a separate control experiment. While information conflict was evident across all experiments, we found differences in task conflict, so the harder it was to perceive the task-irrelevant dimension, the stronger the task conflict became. These findings demonstrate for the first time that task conflict emerges on a continuum, scaling with the level of engagement or processing demands associated with the irrelevant task. Moreover, these results suggest that our ability to inhibit the involuntary activation of an unwanted process is restricted. Therefore, despite the resource-intensive demands of completing the irrelevant task, it still takes place
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