Cranfield University

Isoprene is a reactive hydrocarbon emitted to the atmosphere in large quantities by terrestrial vegetation. Annual total isoprene emissions exceed 300 Tg a⁻¹, but emission rates vary widely among plant species and are sensitive to meteorological and environmental conditions including temperature, sunlight, and soil moisture. Due to its high reactivity, isoprene has a large impact on air quality and climate pollutants such as ozone and aerosols. It is also an important sink for the hydroxyl radical which impacts the lifetime of the important greenhouse gas methane along with many other trace gas species. Modeling the impacts of isoprene emissions on atmospheric chemistry and climate requires accurate isoprene emission estimates. These can be obtained using the empirical Model of Emissions of Gases and Aerosols from Nature (MEGAN), but the parameterization of this model is uncertain due in part to limited field observations. In this study, we use ground‐based measurements of isoprene concentrations and fluxes from 11 field sites to assess the variability of the isoprene emission temperature response across ecosystems. We then use these observations in a Metropolis‐Hastings Markov Chain Monte Carlo (MHMCMC) data assimilation framework to optimize the MEGAN temperature response function. We find that the performance of MEGAN can be significantly improved at several high‐latitude field sites by increasing the modeled sensitivity of isoprene emissions to past temperatures. At some sites, the optimized model was nearly four times more sensitive to temperature than the unoptimized model. This has implications for air quality modeling in a warming climate.

The impact of a changing climate on crop and tree growth remains complex and uncertain. Whilst some areas may benefit from longer growing seasons and increased CO 2 levels, others face threats from more frequent extreme weather events. Models can play a pivotal role in predicting future agricultural and forestry scenarios as they can guide decision-making by investigating the interactions of crops, trees, and the environment. This study used the biophysical EcoYield-SAFE agroforestry model to account for the atmospheric CO 2 fertilization and calibrated the model using existing field measurements and weather data from 1989 to 2021 in a case study in Northern Ireland. The study then looked at two future climate scenarios based on the representative concentration pathways (RCP 4.5 and RCP 8.5) for 2020–2060 and 2060–2100. The predicted net impacts of future climate scenarios on grass and arable yields and tree growth were positive with increasing CO 2 fertilization, which more than offset a generally negative effect of increased temperature and drought stress. The predicted land equivalent ratio remained relatively constant for the baseline and future climate scenarios for silvopastoral and silvoarable agroforestry. Greater losses of soil organic carbon were predicted under arable (1.02–1.18 t C ha ⁻¹ yr ⁻¹ ) than grassland (0.43–0.55 t C ha ⁻¹ yr ⁻¹ ) systems, with relatively small differences between the baseline and climate scenarios. However, the predicted loss of soil organic carbon was reduced in the long-term by planting trees. The model was also used to examine the effect of different tree densities on the trade-offs between timber volume and understory crop yields. To our best knowledge this is the first study that has calibrated and validated a model that accounts for the effect of CO 2 fertilization and determined the effect of future climate scenarios on arable, grassland, woodland, silvopastoral, and silvoarable systems at the same site in Europe.

This study investigates the density and surface tension properties of graphene flake-ethylene glycol (GF-EG) nanofluids. The experimental results demonstrate that the density of GF-EG nanofluids increases with nanoparticle mass fractions while exhibiting a linear decrease with temperature. Surface tension measurements reveal a consistent reduction compared to pure ethylene glycol, aligning with a previously established model that attributes this behavior to nanoparticle saturation at the fluid surface. Notably, the averaged surface tension values for GF-EG nanofluids at 298.15 K were determined to be 47.906 mN $\cdot {\rm m}^{-1}$. A key contribution of this work is the introduction of the concept of a material data table for nanofluids, which aims to consolidate fragmented experimental data into a standardized framework. Such a dataset would enable more accurate prediction of surface tension behavior in different nanofluid systems and facilitate advances in artificial intelligence-based modeling that can identify correlations between nanoparticle characteristics and surface tension, enabling rapid optimization of nanofluids for specific applications. This study not only provides new insights into GF-EG nanofluids in terms of surface tension, but also highlights the transformative potential of artificial intelligence in accelerating the discovery and implementation of next-generation heat transfer fluids.

Industry 4.0 concepts have recently significantly supported transparency and reliability in every industrial sector. Organizations must adapt their traditional paradigms and approaches to align with market demands. Hence, developing a framework that can change these conventional approaches with fresh ideas is essential. The Industry 4.0 (I4.0) readiness model presents a creative concept that holds promise for the entire organizational and industrial value chain. Existing research focused only on technological, organizational, and environmental aspects. However, in process-extensive industries, like the sugar sector, the process is critical and considerably impacts the business. So, providing a strong framework for such sectors is necessary. The novelty of this paper is putting a process dimension in the TOE framework, which is critical for sugar industries. The study develops the extended framework to assess readiness. Experts have validated the framework as the study enhances it by adding process dimensions. Practitioners can apply the modeling concept to study the readiness framework in various sectors. Consequently, essential findings and recommendations drive the discussion forward. The study highlights opportunities for cross-disciplinary research across sectors.

In this work, we report on the performance of an extensive, building-by-building wastewater surveillance platform deployed across 38 locations of the largest private university system in Mexico, spanning 19 of the 32 states, to detect SARS-CoV-2 genetic materials during the COVID-19 pandemic. Sampling took place weekly from January 2021 and June 2022. Data from 343 sampling sites was clustered by campus and by state and evaluated through its correlation with the seven-day average of daily new COVID-19 cases in each cluster. Statistically significant linear correlations (p-values below 0.05) were found in 25 of the 38 campuses and 13 of the 19 states. Moreover, to evaluate the effectiveness of epidemiologic containment measures taken by the institution across 2021 and the potential of university campuses as representative sampling points for surveillance in future public health emergencies in the Monterrey Metropolitan Area, correlation between new COVID-19 cases and viral loads in weekly wastewater samples was found to be stronger in Dulces Nombres, the largest wastewater treatment plant in the city (Pearson coefficient: 0.6456, p-value: 6.36710⁻⁸), than in the largest university campus in the study (Pearson coefficient: 0.4860, p-value: 8.288x10⁻⁵). However, when comparing the data after urban mobility returned to pre-pandemic levels, correlation levels in both locations became comparable (0.894 for the university campus and 0.865 for Dulces Nombres). This work provides a basic framework for the implementation and analysis of similar decentralized surveillance platforms to address future sanitary emergencies, allowing for an efficient return to priority in-person activities while preventing university campuses from becoming transmission hotspots.

This commentary is the second in a two-part series on Environmental Stewardship Education (ESE) in Tuvalu. While Part 1 examined the alignment between education and environmental policies, this follow-up focuses on how those policies are—or are not—translated into formal curriculum and classroom practice. Drawing on both academic research and professional experience in government, this article explores the gap in curriculum design, student engagement, and teaching strategies. It argues for the early integration of ESE in primary education, greater inclusion of traditional ecological knowledge, and participatory teaching approaches. These insights are grounded in Tuvalu’s context but offer valuable lessons for other small island developing states striving to align sustainability policy with educational delivery.

High-throughput and portable sensor technologies are increasingly used in food production/distribution tasks as rapid and non-invasive platforms offering real-time or near real-time monitoring of quality and safety. These are often coupled with analytical techniques, including machine learning, for the estimation of sample quality and safety through monitoring of key physical attributes. However, the developed predictive models often show varying degrees of accuracy, depending on food type, storage conditions, sensor platform, and sample sizes. This work explores various fusion approaches for potential predictive enhancement, through the summation of information gathered from different observational spaces: infrared spectroscopy is supplemented with multispectral imaging for the prediction of chicken and beef spoilage through the estimation of bacterial counts in differing environmental conditions. For most circumstances, at least one of the fusion methodologies outperformed single-sensor models in prediction accuracy. Improvement in aerobic, vacuum, and mixed aerobic/vacuum chicken spoilage scenarios was observed, with performance enhanced by up to 15%. The improved cross-batch performance of these models proves an enhanced model robustness using the presented multi-sensor fusion approach. The batch-based results were corroborated with a repeated nested cross-validation approach, to give an out-of-sample generalised view of model performance across the whole dataset. Overall, this work suggests potential avenues for performance improvements in real-world, minimally invasive food monitoring scenarios.

Although machine Learning has demonstrated exceptional applicability in thermographic inspection, precise defect reconstruction is still challenging, especially for complex defect profiles with limited defect sample diversity. Thus, this paper proposes a self-enhancement defect reconstruction technique based on cycle-consistent generative adversarial network (Cycle-GAN) that accurately characterises complex defect profiles and generates reliable artificial thermal images for dataset augmentation , enhancing defect characterisation. By using a synthetic dataset from simulation and experiments, the network overcomes the limited samples problem by learning the diversity of complex defects from finite element modelling and obtaining the thermography uncertainty patterns from practical experiments. Then, an iterative strategy with a self-enhancement capability optimises the characterisation accuracy and data generation performance. The designed loss function structure with cycle consistency and identity loss constrains the GAN's transfer variation to guarantee augmented data quality and defect reconstruction accuracy simultaneously, while the self-enhancement results significantly improve accuracy in thermal images and defect profile reconstruction. The experimental results demonstrate the feasibility of the proposed method by attaining high accuracy with optimal loss norm for defect profile reconstruction with a Recall score over 0.92. The scalability investigation of different materials and defect types is also discussed, highlighting its capability for diverse thermography quantification and automated inspection scenarios. A cyclic self-enhancement technique for complex defect profile reconstruction based on thermographic evaluation, Acta Mech. Sin. 41, 424076 (2025), https://doi.

Prostate cancer remains the most common male cancer; however, treatment regimens remain unclear in some cases due to a lack of agreement in current testing methods. Therefore, there is an increasing need to identify novel biomarkers to better counsel patients about their treatment options. Microcalcifications offer one such avenue of exploration. Microfocus spectroscopy at the i18 beamline at Diamond Light Source was utilised to measure X-ray diffraction and fluorescence maps of calcifications in 10 µm thick formalin fixed paraffin embedded prostate sections. Calcifications predominantly consisted of hydroxyapatite (HAP) and whitlockite (WH). Kendall’s Tau statistics showed weak correlations of ‘a’ and ‘c’ lattice parameters in HAP with GG (rτ = − 0.323, p = 3.43 × 10–4 and rτ = 0.227, p = 0.011 respectively), and a negative correlation of relative zinc levels in soft tissue (rτ = − 0.240, p = 0.022) with GG. Negative correlations of the HAP ‘a’ axis (rτ = − 0.284, p = 2.17 × 10–3) and WH ‘c’ axis (rτ = − 0.543, p = 2.83 × 10–4) with pathological stage were also demonstrated. Prostate calcification chemistry has been revealed for the first time to correlate with clinical markers, highlighting the potential of calcifications as biomarkers of prostate cancer.

The objective of this study is to evaluate the mummified remains of eight high-ranking people buried in two crypts of the Evangelical Reformed Church at Kėdainiai, Lithuania. The evaluation criteria include biological or cultural indicators, the assessment of pathological conditions and their possible etiology, and the preservation status of these remains. The eight individuals were recovered during a project aimed at exploring the tombs of potential members of the Radziwiłł family, a powerful dynasty of the former Grand Duchy of Lithuania and the Crown of the Kingdom of Poland (1569–1795). However, the remains could also belong to other affluent citizens of Kėdainiai who were buried in the same church between the 17th and 18th centuries. The deceased were investigated using classical anthropological methods and computed tomography, which allowed for a more nuanced vision of both individual social status and bio-histories for this assemblage. The results identify one case of post-mortem manipulation, evidence of significant pathological changes, including degenerative joint disease, lung and arterial calcifications, and neoplasias that would not have been visible without a paleoradiological approach. The historical context, as well as comparative clinical cases, helped narrow down the diagnoses proposed for the lesions concerned, and will be crucial to address additional histological or biomolecular research, should this be carried out in the future. Additionally, the study highlights the need for regular monitoring of the remains, particularly given the evident decay observed over the past four decades. This adds to the body of research suggesting that the more frequent inspection of individuals in which socioeconomic status can be assumed through mortuary context is warranted. In sum, this investigation shows that paleopathology, coupled with paleoradiology, provides a more permanent data set that enhances the interpretation of pathological conditions in preserved bodies, especially when they are in physical danger due to environmental or political changes.

The decomposition mechanisms of double base rocket propellants are well known and reported, but the influence of atmospheric conditions such as water and oxygen is poorly understood. In this work, the influence of water and oxygen on an extruded double base (EDB) rocket propellant aged between 70 and 100°C was examined using heat flow calorimetry. The results show that increased water content and higher oxygen concentration both lead to elevated heat flow. The activation energy (E a ), which was determined using the Friedman differential isoconversional method, showed that Ea decreases with higher water content, suggesting water acts as a catalyst, while lower oxygen concentration increases Ea, indicating slower decomposition. Factorial design analysis confirmed that both factors negatively impact E a , with oxygen being the more significant factor while the water/oxygen interaction (AB) on the E a is negligible. This research provides critical insight into the factors affecting the stability and shelf life of rocket propellants, which can lead to improved formulations and storage conditions, augmenting the safety and performance of EDB rocket propulsion systems. This research provides critical insight into the factors affecting the stability and shelf life of rocket propellants, which can lead to improved formulations and storage conditions, augmenting the safety and performance of EDB rocket propulsion systems.

Specific Target Organ Toxicity - Repeated Exposure (STOT-RE) is a hazard class in both Globally Harmonized System and Classification, Labelling and Packaging (CLP) Regulation in the European Union (EU) legislation on hazard classification labeling and packaging of substances and mixtures. This legislation, used for the chemical safety assessment under the EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), evaluates long-term exposure of chemicals on human or animals and designates three categories of classification - STOT RE 1 (potential to produce significant toxicity to humans); STOT RE 2 (presumed to be toxic to humans), or not classified. Human epidemiologic studies identified neurologic effects as the most sensitive adverse health effect following repeated manganese (Mn) exposure. However, there are inadequate human studies to assess the neurotoxicity and STOT-RE classification of the chloride, sulfate, and nitrate forms of Mn. This review summarizes peer-reviewed studies with original data identified from searches of PubMed and OECD studies submitted as part of the REACH information requirement. This review included peer-reviewed studies that exhibited a duration of ≥21 days, including oral or inhalation exposure, and reported neurobehavioral, neurochemical or neuropathologic outcomes. A total of 75, 6, and 0 investigations met the inclusion criteria for this review for the chloride, sulfate, and nitrate forms of Mn, respectively. Based upon retrieved data or read-across principles a proposed classification of these Mn salts, following repeated oral or inhaled exposure, is STOT RE 2, target organ, the brain.

Droplet transfer has a strong interrelationship with melt pool behavior, significantly influencing bead morphology and the quality of printed parts in the wire and arc additive manufacturing (WAAM) process. However, this relationship is minimally explored in experiments. To address this gap, this study, for the first time, investigates the mechanism behind droplet transfer, melt pool behavior, and bead profile evolution in the WAAM process using a tungsten inert gas power source. The effects of wire positioning were analyzed, revealing that variations in wire location altered heat transfer dynamics, leading to changes in cooling rates and convection flow, and then changing the bead profile. The findings indicated that when the wire was positioned 4 mm above the surface, the melt pool maintained a regular profile. However, when the wire was placed directly on the surface, the melt pool took on an irregular, teardrop-like shape—wider at the front and significantly narrower at the tail. This irregularity resulted from the formation of a bridge current (by-pass current) between wire and arc, which modified heat transfer patterns. Consequently, a dual convection flow emerged, directing molten material from the center to the edges, particularly at the front of the melt pool. These insights contribute to a deeper understanding of the physical mechanisms governing the WAAM process.

Flower strips can provide many economic benefits in commercial orchards, including reducing crop damage by a problematic pest, rosy apple aphid ( Dysaphis plantaginea [Passerini]). To explore the financial costs and benefits of this effect, we developed a bio‐economic model to compare the establishment and opportunity costs of perennial wildflower strips with benefits derived from increased yields due to reduced D. plantaginea fruit damage under high and low pest pressure. This was calculated across three scenarios: (1) a flower strip on land that would otherwise be an extension of the standard grass headland, (2) a flower strip on land that could otherwise be used to produce apples and (3) a flower strip in the centre of an orchard. Through reduction of D. plantaginea fruit damage alone, our study shows that flower strips on the headland can be a positive financial investment. If non‐crop land was not available, establishment of a flower strip in the centre of an orchard, instead of the edge, could recoup opportunity costs by providing benefits to crops on both sides of the flower strip. Our study can help guide the optimal placement of flower strips and inform subsidy value for these schemes.

The plant-based food industry is at a decisive point, facing a set of problems interdependently with increasing opportunities. This chapter covers the complex nature of the industry and examines its challenges and opportunities. Today’s consumers are increasingly opting for plant-based products, but long-standing tendencies and doubts are still on the table and problematic. In addition, crop yields vary seasonally, as do regulatory issues, including difficulties in the supply chain. However, these are some outstanding difficulties, and some possible opportunities are difficult to find in this situation. Consumer trends in health and wellness are also factored into sustainability, where cheap but nutritious products are in demand. Through improved technology, plant-based products that are very similar to animal-based products are produced, making more people want to consume plant-based products. However, worrying about the destructive intrusion of cultures different from our own and seeking new opportunities in global markets are other untapped opportunities for development and creativity. These are some challenges the plant-based food industry has to deal with to evolve into a more sustainable and inclusive food system.

Plain Language Summary Some soils store carbon not just from plants but also from the rocks beneath them. This rock‐derived carbon, called petrogenic organic carbon (OCpetro), comes from certain types of bedrock (e.g., shale). Most current models of soil carbon focus only on plant‐derived carbon, ignoring the role of OCpetro. This oversight could mean we are underestimating the total carbon stored in soils, especially in deeper layers. Furthermore, the geologic age of OCpetro means that it may significantly impact ¹⁴C signatures of soil OC, and hence apparent carbon turnover times. Additionally, we still don't fully understand how OCpetro might affect soil microbes or how land management practices influence this type of carbon. To address these gaps, researchers need to explore how OCpetro moves from bedrock into soil and how stable it is once there. This requires collaboration between scientists studying soils, rocks, and ecosystems. Answering key questions about OCpetro's behavior could improve our understanding of soil carbon and help us manage soils more effectively for climate change mitigation. Including OCpetro in soil carbon models is a critical step toward better predictions of soil carbon storage and its role in the global carbon cycle.

Preserving historical railway assets presents a complex systems challenge, in which uncertainties in material performance, structural degradation, and regulatory requirements directly impact long-term reliability and operational continuity. Traditional maintenance practices often limit the use of modern materials, introducing inefficiencies, increased lifecycle costs, and higher failure risk due to material ageing and environmental exposure. This study proposes a reliability-informed preservation framework that supports the integration of contemporary materials into historical railway infrastructure while accounting for legal, material, and procedural uncertainties. The framework is validated through two industrial case studies, each reflecting different regulatory and operational constraints. The first case demonstrates the successful substitution of timber with certified PVC cladding on a non-listed signal box, achieving improved durability, reduced maintenance intervals, and enhanced system reliability. The second case explores an unsuccessful attempt to replace decayed timber gables with aluminium, in which late-stage planning misalignment, underestimated risks, and uncertainty in approval outcomes led to a significant cost increase and reduced reliability regarding delivery. By systematically applying and evaluating the framework under real-world conditions, this research contributes to engineering asset management by introducing a structured method for mitigating regulatory and material uncertainties.