University of Lincoln

Demand for animal-source foods (ASF) is growing globally, and the consumption can improve food and nutrition security. However, there are growing food safety risks associated with milk contamination. Studies assessing food safety measures (FSM) at the farm level are still limited. This study investigated FSM adoption in Kenyan smallholder dairy farms. Data were collected through focus group discussions (FGDs) and a farm survey involving 652 farmers, which considered 11 milking hygiene, 6 milk storage, 6 environmental hygiene, and 7 animal health measures. The Food Safety Index (FSI) was 51.67%, which reveals average FSM adoption and a good agricultural practices (GAPs) compliance gap that exposes consumers to public health risks. FSM adoption was associated with participation in formal and/or informal milk value chains, socio-economic and demographic factors, knowledge of milk quality standards and regulations, farm biophysical conditions, market dynamics and institutional factors. There is a need for increasing FSM adoption through improving farmers' knowledge, supporting their access to financial resources, and providing infrastructure and services, including roads, inputs, extension and animal health services. Policymakers should design and implement policies that address specific farmers' needs. There is also a need to provide economic incentives and ensure that the market rewards dairy producers who adopt high levels of FSM that lead to safer milk. Finally, promoting the One Health approach can help farmers address human, environmental and animal health risks, which can reduce food safety risks in dairy value chains.

Ammonium acetate (AAc)–methanesulfonic acid (MSA) deep eutectic solvents (DESs) represent a promising class of green, tunable media with properties that can be tailored for future hydrometallurgical applications. This work reports the formulation and systematic characterisation of AAc–MSA DESs across molar ratios from 1:5 to 1:9 (AAc:MSA), using Fourier Transform Infrared (FTIR) spectroscopy, Raman spectroscopy, Proton Nuclear Magnetic Resonance ( 1 H NMR) spectroscopy, and Ultraviolet–Visible (UV–Vis) spectroscopy to investigate molecular interactions and acidity profiles relevant to potential metal extraction applications. FTIR and Raman analyses indicate the structural transition from hydrogen-bond-dominated networks to ionically enriched environments with increasing MSA concentration due to interactions between AAc's ammonium (NH 4 + )/acetate (CH 3 COO − ) moieties and the sulfonate group (–SO 3 − ) of MSA. Key vibrational shifts (N–H/O–H stretching at 3219.68–3231.91 cm −1 ; SO 2 stretching at 1137.80–1154.11 cm −1 ) confirm weakened hydrogen bonding and increased ionic character with rising MSA content. The 1 H NMR supports this structural transition, showing upfield shifts in NH 4 + protons (7.30 to 6.79–7.04 ppm) and downfield shifts in –SO 3 − protons (12.86 to 13.26 ppm from 1:5 to 1:8), indicating an MSA-dominated ionic environment. The Hammett acidity function ( H 0 ) demonstrates strong tunability and the emergence of superacidity, ranging from H 0 = −5.385 to −5.991 in pure DESs, and from 1.569 to −0.096 in the diluted systems, depending on concentration. These findings demonstrate that AAc–MSA DESs possess the structural and acidity properties required for proton-driven mineral breakdown, highlighting their potential for sustainable metal extraction and selective ion recovery. • Novel AAc–MSA DESs formulated and characterized for sustainable metal recovery. • Spectroscopic analysis via FTIR, Raman, and 1 H NMR reveals a structural transition from hydrogen-bonded to ionic networks. • Superacidity of pure AAc–MSA DESs with Hammett function ( H 0 ) values reaching −5.991. • Tunable acidity demonstrated in aqueous environments ( H 0 = 1.569 to −0.096), enabling selective metal recovery. • Sustainable alternative to mineral acids validated for high-performance, proton-driven metallurgical leaching.

Corporate governance reforms in emerging and frontier markets frequently assume that strengthening board oversight, audit committees, and ownership monitoring will improve audit quality and enhance firm value. Yet, in weak institutional environments, these mechanisms often function symbolically rather than substantively. This study rethinks the governance–audit–value nexus by integrating Agency Theory, Institutional Theory, and the concept of symbolic governance to explain why governance may appear structurally robust while failing to constrain managerial discretion. Using panel data from Ghanaian listed firms between 2015 and 2023, the analysis shows that audit committee independence and board independence are negatively associated with both audit quality and firm value, indicating that formal independence without expertise, authority, or enforcement capacity does not translate into meaningful oversight. By contrast, institutional and managerial ownership positively influence both outcomes, suggesting that incentive alignment and informed monitoring can substitute for weak formal governance. Foreign ownership improves firm value but does not consistently enhance audit quality, while macroeconomic conditions such as inflation and GDP growth further shape firm performance. The study advances the literature by reconceptualising governance effectiveness in weak institutional environments, demonstrating that governance mechanisms may exist in form without functioning in substance. The findings underscore the need for governance reforms that prioritise enforcement capacity, board expertise, and audit committee competence rather than structural compliance alone.

Understanding the coupling between river discharge (Q) and suspended sediment concentration (SSC) is fundamental for understanding erosion, sediment connectivity, and sediment sources in a fluvial system. While direct correlation via rating curves provides a first-order approximation of the discharge-sediment relationship, it cannot capture the non-linear response of SSC to changes in Q during a hydrological event or on an annual timescale. Hysteresis process, i.e., the difference in SSC for a given Q during rising and falling limbs, is utilized for characterizing such nonlinearities in the Q and SSC relationship. While Hysteresis Loop (HL) is the graphical representation of the Q-SSC relationship, Hysteresis Index (HI) is the quantitative measure of the broadness of HL. HL and HI are frequently used to understand the time lags, sediment sources, and sediment transport capacity within river reaches. Several approaches to HI quantification are present in the literature, indicating methodological variability and data normalization techniques, whereas a systematic comparison is still required to understand the applicability of each model for specific research problems. This work presents a comprehensive review of major methodologies for calculating HI from Q and SSC data for different timescales. We employed daily Q and SSC data from the Monsoon-dominated Peninsular Indian region and Moravian-Silesian Region. Further, we used high-resolution (15-minute) Q and SSC data from the Rio Cordon catchment in the Eastern Italian Alps. The daily Q and SSC values were aggregated into mean monthly values to observe the annual-scale hysteresis patterns in Peninsular India. We developed a MATLAB toolbox, ‘Hysteresis Index Toolbox (HyInd)’, to standardize the normalization technique, rising and falling limb separation for hydrological events, and data visualization. The toolbox currently features six methods for HI calculation, which are suitable for all time scales (sub-daily, daily, or monthly). Our results suggest that data normalization is crucial for comparing sediment transport dynamics across drainage basins with varying drainage areas. Furthermore, our results also imply that quantification of the normalized area inside the HL presents the most robust result for simple to complex hysteresis processes. We also carried out sensitivity analyses to assess the influence of data noise on the quantification of HI. While sub-daily Q and SSC data inherently contain noise, we introduced stochastic perturbations into the daily and monthly average datasets. We observed that most existing HI calculation methods are not significantly affected by random noise. This study elucidates the strengths and weaknesses of each HI calculation method and provides practical guidance for selecting the proper model. Although the HyInd toolbox is primarily designed to understand the discharge-sediment hysteresis process, it can also be used to quantify hysteresis in other environmental parameters, i.e., soil moisture or water quality.

This study investigates the potential of large language model (LLM)-enabled robots to support inclusive child-robot storytelling activities in elementary education. 65 children (ages 6–11) engaged in story co-construction with an LLM-enabled Pepper robot across two experimental conditions. In the child-robot dyad, the robot autonomously guided the interaction through questioning and cartoon displays. In the child-adult-robot triad, Pepper displayed story visuals while an adult co-facilitated dialogue. Evidence-based, neuro-affirming strategies were embedded throughout. Parents completed the Autism Spectrum Quotient (AQ-10), while children provided post-session self-reports on engagement and affect. Interaction transcripts were analysed for structural linguistic features. Results showed that both conditions effectively supported story recall and engagement. Affect toward the robot was consistently high, though a negative correlation with age in the dyad condition suggested increased awareness of technical limitations. Linguistic analyses revealed no differences in grammatical complexity between conditions, yet triadic sessions fostered broader vocabulary use and greater recall for older children. Outcomes correlation with AQ-10 indicate that both the autonomous robot and the human-robot team were effective and engaging across the neurodiverse spectrum. Findings demonstrate that LLM-enabled robots can be both standalone educational partners and tools for hybrid human-robot facilitation. We discuss implications for inclusive pedagogy, highlight technical challenges, and propose future directions for research.

Limited male engagement in HIV prevention and care is a global challenge more pronounced in sub-Saharan Africa (SSA) contributing to ongoing transmission. However, peer network interventions show promise in improving engagement. To map and synthesise evidence on peer network interventions for HIV prevention and care among men in SSA, with a focus on the types of strategies used, populations reached and how these interventions address cultural, social and structural barriers across the HIV care cascade. Scoping review of peer-reviewed literature, conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Community and facility-based HIV prevention and care settings across multiple countries in SSA. Populations included men having sex with men, men in key occupational groups (fishermen, truck drivers), adolescents and young people, and men living with HIV. Studies not conducted in SSA, not peer-reviewed or not focused on male peer networks were excluded. Peer network interventions included peer education, peer navigation, HIV self-testing (HIVST) distribution, adherence support groups, disclosure and stigma-reduction counselling, technology-enabled peer support (SMS and social media) and community-based antiretroviral therapy (ART) delivery. Intervention duration and intensity varied across studies. Primary outcomes included HIV testing uptake, linkage to care, ART initiation, adherence, retention in care and viral suppression. Secondary outcomes included stigma reduction, disclosure and engagement among hard-to-reach male populations. We conducted a review of literature published between November 2013 and November 2024, searching PubMed, Web of Science, Scopus and Cochrane databases. Studies were included if they examined peer network approaches in HIV prevention and care among men in SSA. A total of 905 records were identified, of which 75 studies met the inclusion criteria. Peer network interventions were implemented across diverse SSA contexts and male populations. Strategies such as peer-led education, social diffusion models, HIVST distribution and technology-enabled peer support consistently improved HIV testing uptake, linkage to care, ART initiation and adherence. Contextually tailored interventions such as community-based outreach addressing occupational risk environments and economic vulnerabilities were particularly effective in engaging men traditionally underserved by facility-based services. However, challenges persisted, including variable linkage to care following HIVST and sensitivity to user costs. Peer-led interventions in HIV care for men in SSA effectively address cultural, social and structural barriers, improving testing, ART adherence and viral suppression. Tailored, technology-enhanced and community-based approaches ensure equitable HIV prevention and treatment outcomes, despite challenges like linkage to care.

Algorithm-based fertiliser recommendations offer substantial potential to improve Nitrogen Use Efficiency (NUE) and support economic and environmental sustainability. However, adoption among farmers in the United Kingdom (UK) remains limited, partly due to algorithm aversion, i.e., the tendency to distrust or avoid algorithmic-generated recommendations, even when they provide benefits. This study examines algorithm aversion in fertiliser-related decision-making among UK farmers and agronomists. Aiming to identify key barriers to adopting decision-support tools (DSTs), improving understanding of stakeholder trust dynamics, and exploring strategies to improve uptake. An online survey of 50 farmers and 26 agronomists assessed confidence in algorithmic recommendations versus human advice, understanding of NUE, perceived adoption barriers, and openness to non-traditional fertiliser recommendations. A follow-up workshop with 10 participants in DSTs trials provided qualitative insights into trust and usability. Farmers reported significantly greater trust in human advice compared to algorithmic recommendations (median 8 vs. 6, p < .001), whereas agronomists showed the reverse pattern (median 8 vs. 7.0, p < .001). Perceived barriers included cost concerns, poor system integration, complexity, and confusion over metrics. Whilst some farmers showed low levels of NUE literacy, agronomists demonstrated higher NUE literacy. Farmers relied on advice grounded in social trust and shared beliefs, while agronomists viewed algorithmic outputs as complements to technical expertise. Workshop participants found DST dashboards informative but often overwhelming. Addressing algorithm aversion through improved interface design, transparency, and tailored education, particularly via trusted advisors, may bridge the trust gap and facilitate digital tool adoption. • Farmers show significant algorithm aversion in fertiliser-related decision-making. • Agronomists are more receptive to algorithmic tools than farmers. • Perceived barriers to use include cost, poor usability, low trust, and unfamiliar metrics. • Trusted advisors may play a role in supporting farmer adoption of digital decision-support tools. • Addressing algorithm aversion has potential to improve existing on-farm practices.